Validate modification of audio-based computer program output

ABSTRACT

Modifying computer program output in a voice or non-text input activated environment is provided. A system can receive audio signals detected by a microphone of a device. The system can parse the audio signal to identify a computer program to invoke. The computer program can identify a dialog data structure. The system can modify the identified dialog data structure to include a content item. The system can provide the modified dialog data structure to a computing device for presentation. The system can validate the dialog data structure output by the computing device for presentation.

BACKGROUND

Excessive network transmissions, packet-based or otherwise, of networktraffic data between computing devices can prevent a computing devicefrom properly processing the network traffic data, completing anoperation related to the network traffic data, or timely responding tothe network traffic data. The excessive network transmissions of networktraffic data can also complicate data routing or degrade the quality ofthe response if the responding computing device is at or above itsprocessing capacity, which may result in inefficient bandwidthutilization. The control of network transmissions corresponding tocontent item objects can be complicated by the large number of contentitem objects that can initiate network transmissions of network trafficdata between computing devices.

SUMMARY

The present disclosure is generally directed to improving the efficiencyand effectiveness of information transmission and processing overdisparate computing resources. It is challenging for disparate computingresource to efficiently process, and consistently and accurately provideaudio-based content items in a voice-based (or other non-text based suchas image or video) computing environment. For example, the disparatecomputing resources may not have access to the same voice or imagemodels, or may have access to out of date or unsynchronized voice orimage models that can make it challenging to accurately and consistentlyprovide the audio-based content item. Further, the computing resourcesmay perform redundant processing to select content items that can bereused, thereby reducing processor utilization.

Systems and methods of the present disclosure are generally directed toa data processing system that modifies computing program output via anetwork. The data processing system can receive a request for contentthat is to be provided via a computing program comprising a chatbot. Achatbot, or artificial conversational entity, can refer to a computerprogram that conducts a conversation via auditory, visual, or textualtechniques.

At least one aspect is directed to a system to modify computer programoutput. The system can include a data processing system comprising oneor more processors and memory. The data processing system can receive,from a computing device, a digital file corresponding to a firstacoustic signal carrying voice content detected by a microphone of thecomputing device. The first acoustic signal can be converted to thedigital file by an analog to digital converter of the computing device.The data processing system can select, responsive to the voice contentof the digital file, a computer program comprising a chatbot from aplurality of computer programs comprising chatbots for execution. Thedata processing system can identify, via the chatbot based on the voicecontent of the digital file, a dialog data structure comprising aplaceholder field. The data processing system can generate, responsiveto identification of the placeholder field in the dialog data structure,a request for content in a parameterized format configured for aparametrically driven text to speech technique. The data processingsystem can transmit the request for the content to a content selectioncomponent of the data processing system. The data processing system canselect, via a content selection process responsive to the request, acontent item for insertion into the placeholder field of the dialog datastructure. The content item can be in the parameterized formatconfigured for the parametrically driven text to speech technique. Thedata processing system can provide, to the chatbot, the content item inthe parameterized format selected via the content selection process tocause the computing device to perform the parametrically driven text tospeech technique to generate a second acoustic signal corresponding tothe dialog data structure modified with the content item.

At least one aspect is directed to a method of modifying computerprogram output. The method can be performed by a data processing systemcomprising one or more processors and memory. The method can include thedata processing system receiving, from a computing device, a digitalfile corresponding to a first acoustic signal carrying voice contentdetected by a microphone of the computing device. The first acousticsignal can be converted to the digital file by an analog to digitalconverter of the computing device. The method can include the dataprocessing system selecting, responsive to the voice content of thedigital file, a computer program comprising a chatbot from a pluralityof computer programs comprising chatbots for execution. The method caninclude the data processing system identify, via the chatbot based onthe voice content of the digital file, a dialog data structurecomprising a placeholder field. The method can include the dataprocessing system generating, responsive to identification of theplaceholder field in the dialog data structure, a request for content ina parameterized format configured for a parametrically driven text tospeech technique. The method can include the data processing systemtransmitting the request for the content to a content selectioncomponent of the data processing system. The method can include the dataprocessing system selecting, via a content selection process responsiveto the request, a content item for insertion into the placeholder fieldof the dialog data structure. The content item can be in theparameterized format configured for the parametrically driven text tospeech technique. The method can include the data processing systemproviding, to the chatbot, the content item in the parameterized formatselected via the content selection process to cause the computing deviceto perform the parametrically driven text to speech technique togenerate a second acoustic signal corresponding to the dialog datastructure modified with the content item.

At least one aspect is directed to a method of modifying computerprogram output. The method can include a sensor of a computing devicedetecting a first image comprising visual content. The method caninclude the computing converting the first image to a digital filecorresponding to the visual content. The method can include selecting,responsive to the visual content of the digital file, a computer programcomprising a chatbot from a plurality of computer programs comprisingchatbots for execution. The method can include identifying, by thechatbot based on the visual content of the digital file, a dialog datastructure comprising a placeholder field. The method can includegenerating, responsive to identifying the placeholder field in thedialog data structure, a request for content in a parameterized formatconfigured for a parametrically driven text to speech technique. Themethod can include transmitting, by the chatbot, the request for thecontent to a content selection server. The method can include selecting,by the content selection server responsive to the request, a contentitem for insertion into the placeholder field of the dialog datastructure, the content item in the parameterized format configured forthe parametrically driven text to speech technique. The method caninclude providing, to the chatbot, the content item in the parameterizedformat selected via the content selection process to cause the computingdevice to perform the parametrically driven text to speech technique togenerate an acoustic signal corresponding to the dialog data structuremodified with the content item.

At least one aspect is directed to a system to balance data requests formodification of computer program output. The system can include a dataprocessing system comprising one or more processors and memory. The dataprocessing system can receive, from a computing device, a first digitalfile corresponding to a first acoustic signal with first voice contentdetected by a microphone of the computing device. The first acousticsignal converted to the first digital file by an analog to digitalconverter of the computing device. The data processing system canselect, responsive to the first voice content of the first digital file,a computer program comprising a chatbot from a plurality of computerprograms comprising chatbots for execution. The data processing systemcan identify, via the chatbot based on the first voice content of thefirst digital file, a first dialog data structure comprising aplaceholder field. The data processing system can select, via a contentselection process responsive to identification of the first placeholderfield in the first dialog data structure, a content item for insertioninto the first placeholder field of the first dialog data structure. Thecontent item can be in a parameterized format configured for aparametrically driven text to speech technique. The data processingsystem can provide, to the chatbot, the content item in theparameterized format selected via the content selection process to causethe computing device to perform the parametrically driven text to speechtechnique to generate a second acoustic signal corresponding to thefirst dialog data structure modified with the content item. The dataprocessing system can generate an index value based on a firstidentifier of the chatbot, a second identifier for the first dialog datastructure, and a third identifier for the computing device. The dataprocessing system can associate, in the memory, the content item withthe index value. The data processing system can receive a second digitalfile corresponding to a third acoustic signal carrying second voicecontent detected by the microphone on the computing device. The dataprocessing system can select, responsive to the second voice content ofthe second digital file, the computer program comprising the chatbot.The data processing system can identify, via the chatbot based on thesecond voice content of the second digital file, a second dialog datastructure comprising a second placeholder field. The data processingsystem can select, responsive to identification of the secondplaceholder and based on the first identifier of the chatbot, the thirdidentifier of the computing device, and a fourth identifier of thesecond dialog data structure, the content item associated with the indexvalue. The data processing system can provide, to the chatbot, thecontent item associated with the index value to cause the computingdevice to perform the parametrically driven text to speech technique togenerate a fourth acoustic signal corresponding to the second dialogdata structure modified with the content item.

At least one aspect is directed to a method of balancing data requestsfor modification of computer program output. The method can be performedby a data processing system comprising one or more processors andmemory. The method can include the data processing system receiving,from a computing device, a first digital file corresponding to a firstacoustic signal with first voice content detected by a microphone of thecomputing device. The first acoustic signal can be converted to thefirst digital file by an analog to digital converter of the computingdevice. The method can include the data processing system selecting,responsive to the first voice content of the first digital file, acomputer program comprising a chatbot from a plurality of computerprograms comprising chatbots for execution. The method can include thedata processing system identifying, via the chatbot based on the firstvoice content of the first digital file, a first dialog data structurecomprising a placeholder field. The method can include the dataprocessing system selecting, via a content selection process responsiveto identification of the first placeholder field in the first dialogdata structure, a content item for insertion into the first placeholderfield of the first dialog data structure. The content item can be in aparameterized format configured for a parametrically driven text tospeech technique. The method can include the data processing systemproviding, to the chatbot, the content item in the parameterized formatselected via the content selection process to cause the computing deviceto perform the parametrically driven text to speech technique togenerate a second acoustic signal corresponding to the first dialog datastructure modified with the content item. The method can include thedata processing system generating an index value based on a firstidentifier of the chatbot, a second identifier for the first dialog datastructure, and a third identifier for the computing device. The methodcan include the data processing system associating, in the memory, thecontent item with the index value. The method can include the dataprocessing system receiving a second digital file corresponding to athird acoustic signal carrying second voice content detected by themicrophone on the computing device. The method can include the dataprocessing system selecting, responsive to the second voice content ofthe second digital file, the computer program comprising the chatbot.The method can include the data processing system identifying, via thechatbot based on the second voice content of the second digital file, asecond dialog data structure comprising a second placeholder field. Themethod can include the data processing system selecting, responsive toidentification of the second placeholder and based on the firstidentifier of the chatbot, the third identifier of the computing device,and a fourth identifier of the second dialog data structure, the contentitem associated with the index value. The method can include the dataprocessing system providing, to the chatbot, the content item associatedwith the index value to cause the computing device to perform theparametrically driven text to speech technique to generate a fourthacoustic signal corresponding to the second dialog data structuremodified with the content item.

At least one aspect is directed to a system to balance data requests formodification of computer program output. The system can include a dataprocessing system comprising one or more processors and memory. The dataprocessing system can receive, from a computing device, a first digitalfile corresponding to a first acoustic signal with first voice contentdetected by a microphone of the computing device. The first acousticsignal can be converted to the first digital file by an analog todigital converter of the computing device. The data processing systemcan select, responsive to the first voice content of the first digitalfile, a computer program comprising a first chatbot from a plurality ofcomputer programs comprising chatbots for execution. The data processingsystem can identify, via the first chatbot based on the first voicecontent of the first digital file, a first dialog data structurecomprising a placeholder field. The data processing system can select,via a content selection process responsive to identification of thefirst placeholder field in the first dialog data structure, a contentitem for insertion into the first placeholder field of the first dialogdata structure. The content item can be in a parameterized formatconfigured for a parametrically driven text to speech technique. Thedata processing system can provide, to the computing device, the firstdialog data structure modified with the content item in theparameterized format selected via the content selection process to causethe computing device to perform the parametrically driven text to speechtechnique to generate a second acoustic signal corresponding to thefirst dialog data structure modified with the content item. The dataprocessing system can generate an index value based on a firstidentifier for the first placeholder field and a second identifier forthe computing device. The data processing system can associate, in thememory, the content item with the index value. The data processingsystem can receive a second digital file corresponding to a thirdacoustic signal carrying second voice content detected by the microphoneon the computing device. The data processing system can select,responsive to the second voice content of the second digital file, asecond computer program comprising a second chatbot from the pluralityof computer programs, the second chatbot different from the firstchatbot. The data processing system can identify, via the second chatbotbased on the second voice content of the second digital file, a seconddialog data structure comprising a second placeholder field. The dataprocessing system can select, responsive to identification of the secondplaceholder and based on the first identifier of the first placeholderfield and the second identifier of the computing device, the contentitem associated with the index value. The data processing system canprovide, to the computing device, the second dialog data structuremodified with the content item associated with the index value to causethe computing device to perform the parametrically driven text to speechtechnique to generate a fourth acoustic signal corresponding to thesecond dialog data structure modified with the content item.

At least one aspect is directed to a method of balancing data requestsfor modification of computer program output. The method can be performedby a data processing system comprising one or more processors andmemory. The method can include the data processing system receiving,from a computing device, a first digital file corresponding to a firstacoustic signal with first voice content detected by a microphone of thecomputing device. The first acoustic signal can be converted to thefirst digital file by an analog to digital converter of the computingdevice. The method can include the data processing system selecting,responsive to the first voice content of the first digital file, acomputer program comprising a first chatbot from a plurality of computerprograms comprising chatbots for execution. The method can include thedata processing system identifying, via the first chatbot based on thefirst voice content of the first digital file, a first dialog datastructure comprising a placeholder field. The method can include thedata processing system selecting, via a content selection processresponsive to identification of the first placeholder field in the firstdialog data structure, a content item for insertion into the firstplaceholder field of the first dialog data structure. The content itemcan be in a parameterized format configured for a parametrically driventext to speech technique. The method can include the data processingsystem providing, to the computing device, the first dialog datastructure modified with the content item in the parameterized formatselected via the content selection process to cause the computing deviceto perform the parametrically driven text to speech technique togenerate a second acoustic signal corresponding to the first dialog datastructure modified with the content item. The method can include thedata processing system generating an index value based on a firstidentifier for the first placeholder field and a second identifier forthe computing device. The method can include the data processing systemassociating, in the memory, the content item with the index value. Themethod can include the data processing system receiving a second digitalfile corresponding to a third acoustic signal carrying second voicecontent detected by the microphone on the computing device. The methodcan include the data processing system selecting, responsive to thesecond voice content of the second digital file, a second computerprogram comprising a second chatbot from the plurality of computerprograms, the second chatbot different from the first chatbot. Themethod can include the data processing system identifying, via thesecond chatbot based on the second voice content of the second digitalfile, a second dialog data structure comprising a second placeholderfield. The method can include the data processing system selecting,responsive to identification of the second placeholder and based on thefirst identifier of the first placeholder field and the secondidentifier of the computing device, the content item associated with theindex value. The method can include the data processing systemproviding, to the computing device, the second dialog data structuremodified with the content item associated with the index value to causethe computing device to perform the parametrically driven text to speechtechnique to generate a fourth acoustic signal corresponding to thesecond dialog data structure modified with the content item.

At least one aspect is directed to a system to validate modification ofcomputer program output. The system can include a data processing systemcomprising one or more processors and memory. The data processing systemcan establish a communication channel with a third-party server thatprovides a computer program comprising a chatbot. The computer programcan include the chatbot selected based on an acoustic signal detected bya microphone of a computing device. The data processing system canreceive, from the third-party server, a request for content in aparameterized format configured for a parametrically driven text tospeech technique. The request can be triggered by identification of aplaceholder field in a dialog data structure identified by the chatbot.The data processing system can select, via a content selection processresponsive to the request, a content item for insertion into theplaceholder field of the dialog data structure. The content item can bein the parameterized format configured for the parametrically driventext to speech technique. The data processing system can transmit, tothe third-party server, the content item in the parameterized formatselected via the content selection process for provision to the chatbotto cause the computing device to perform the parametrically driven textto speech technique to generate a second acoustic signal correspondingto the dialog data structure modified with the content item. The dataprocessing system can receive, from the chatbot, an indication of thecontent item. The data processing system can set, based on a comparisonof the indication of the content item with the content item, avalidation parameter for the third-party server.

At least one aspect is directed to a method of validating modificationof computer program output. The method can be performed by a dataprocessing system comprising one or more processors and memory. Themethod can include the data processing system establishing acommunication channel with a third-party server that provides a computerprogram comprising a chatbot. The computer program can include thechatbot selected based on an acoustic signal detected by a microphone ofa computing device. The method can include the data processing systemreceiving, from the third-party server, a request for content in aparameterized format configured for a parametrically driven text tospeech technique. The request can be triggered by identification of aplaceholder field in a dialog data structure identified by the chatbot.The method can include the data processing system selecting, via acontent selection process responsive to the request, a content item forinsertion into the placeholder field of the dialog data structure. Thecontent item can be in the parameterized format configured for theparametrically driven text to speech technique. The method can includethe data processing system transmitting, to the third-party server, thecontent item in the parameterized format selected via the contentselection process for provision to the chatbot to cause the computingdevice to perform the parametrically driven text to speech technique togenerate a second acoustic signal corresponding to the dialog datastructure modified with the content item. The method can include thedata processing system receiving, from the chatbot, an indication of thecontent item. The method can include the data processing system setting,based on a comparison of the indication of the content item with thecontent item, a validation parameter for the third-party server.

These and other aspects and implementations are discussed in detailbelow. The foregoing information and the following detailed descriptioninclude illustrative examples of various aspects and implementations,and provide an overview or framework for understanding the nature andcharacter of the claimed aspects and implementations. The drawingsprovide illustration and a further understanding of the various aspectsand implementations, and are incorporated in and constitute a part ofthis specification.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings are not intended to be drawn to scale. Likereference numbers and designations in the various drawings indicate likeelements. For purposes of clarity, not every component may be labeled inevery drawing. In the drawings:

FIG. 1 is an illustration of a system to modify computer program outputvia a network.

FIG. 2 is an illustration of an operation of a system to modify computerprogram output via a network.

FIG. 3 is an illustration of an operation of a system to balance datarequests for modification of computer program output based on a session.

FIG. 4 is an illustration of an operation of a system to balance datarequests for modification of computer program output based on a session.

FIG. 5 is an illustration of an operation of a system to validatemodification of computer program output via a network.

FIG. 6 is an illustration of a method of modifying computer programoutput via a computer network.

FIG. 7 is an illustration of a method of balancing data requests tomodify computer program output via a computer network.

FIG. 8 is an illustration of a method of balancing data requests tomodify computer program output via a computer network.

FIG. 9 is an illustration of a method of validating computer programoutput via a computer network.

FIG. 10 is a block diagram illustrating a general architecture for acomputer system that can be employed to implement elements of thesystems and methods described and illustrated herein.

DETAILED DESCRIPTION

Following below are more detailed descriptions of various conceptsrelated to, and implementations of, methods, apparatuses, and systems ofrouting packetized actions via a computer network. The various conceptsintroduced above and discussed in greater detail below may beimplemented in any of numerous ways.

The present disclosure is generally directed to improving the efficiencyand effectiveness of information transmission and processing overdisparate computing resources. It is challenging for disparate computingresources to efficiently process, and consistently and accuratelyprovide audio-based content items in a voice-based computingenvironment. For example, the disparate computing resources may not haveaccess to the same voice models, or may have access to out of date orunsynchronized voice models that can make it challenging to accuratelyand consistently provide the audio-based content item. Further, thecomputing resources may perform redundant processing to select contentitems that can be reused, thereby reducing processor utilization andwasting processing resources and electrical power.

Systems and methods of the present disclosure are generally directed toa data processing system that modifies computing program output via anetwork. The data processing system can receive a request for contentthat is to be provided via a computing program comprising a chatbot. Achatbot, or artificial conversational entity, can refer to a computerprogram that conducts a conversation via auditory, visual, or textualtechniques.

The present solution can reduce resource consumption, processorutilization, battery consumption, bandwidth utilization, size of anaudio file, or amount of time consumed by a speaker by parsingvoice-based instructions from an end user, selecting or reusing aparameterized content item, and routing the parameterized content itemwith a dialog data structure.

For example, the present solution can provide automated native contentitems for chatbots through dynamic digital product placement. Thesolution can provide a configuration of an application programminginterface that allows for the chatbot to initiate or request a contentselection process, as well as insert the selected content item in adialog data structure. For example, the chatbot can be a recipe chatbot.The recipe chatbot can provide a list of ingredients, for example, inthe native voice. The present solution can identify placeholder in theingredient list, select a content item via a content selection processfor the placeholder, and provide the content item for insertion in theplaceholder. The content selection process can occur in real-time, forexample after the chatbot has launched or been executed and prior toplaying the portion of the dialog data structure having the placeholder.The system can perform the content selection process in close proximityto when the placeholder would be rendered. Further, the applicationprogramming interface can use a parametrically driven text to speechtechnique to provide content items using a native voice.

The present solution can merge, resume, or re-establish sessions inorder to reduce data processing. For example, the technology candetermine that a session is to be resumed, and use a content itemselected in a previous session for provision in a second dialog datastructure after a break in the session. For example, the recipe chatbotcan provide the list of ingredients and insert a brand name for aningredient that is selected via a content selection process. The systemcan then detect a break in the session. The system can detect the breakbased on, for example, the user going to the store to purchase theingredients. When the user returns home, the recipe chatbot canre-establish the session and use the previously selected brand name asopposed to a different brand name for the same ingredients. By using thesame brand name, the system is more efficient because the system avoidsperforming the significant processing associated with performing acontent selection process.

The system can resume sessions with the same chatbot, or straddle asession across multiple chatbots. The system can aggregate data amongmultiple chatbots or interface between multiple chatbots in order toreduce processor utilization by avoiding or reducing the amount ofredundant processing. For example, the recipe chatbot can identify abrand name soda. Thereafter, a computing device can invoke a moviechatbot. The movie chatbot can poll or query the recipe chatbot for thebrand name of the soda, and the movie chatbot, using the same nativevoice, or a different native voice as compared to the recipe chatbot,can insert the same brand name of the soda.

The system can validate a chatbot platform using a validation technique.For example, the data processing system can provide a content item to athird-party chatbot server with instructions to forward the content itemto the chatbot for insertion in a dialog data structure. The dataprocessing system can then ping the chatbot for the content item, andcompare the response from the chatbot with the content item that wasinitially provided to the third-party chatbot platform to determinewhether they match, or if the third-party chatbot platform modified thecontent item.

FIG. 1 illustrates an example system 100 to modify computer programoutput via a computer network. The system 100 can include contentselection infrastructure. The system 100 can include a data processingsystem 102. The data processing system 102 can communicate with one ormore of a content provider computing device 106, chatbot provider device108, third-party chatbot platform server 146, or client computing device104 via a network 105. The network 105 can include computer networkssuch as the Internet, local, wide, metro, or other area networks,intranets, satellite networks, and other communication networks such asvoice or data mobile telephone networks. The network 105 can be used toaccess information resources such as web pages, web sites, domain names,or uniform resource locators that can be presented, output, rendered, ordisplayed on at least one computing device 104, such as a laptop,desktop, tablet, personal digital assistant, smart phone, portablecomputers, or speaker. For example, via the network 105 a user of thecomputing device 104 can access information or data provided by achatbot provider 108 or content provider computing device 106. Thecomputing device 104 may or may not include a display; for example, thecomputing device may include limited types of user interfaces, such as amicrophone and speaker. In some cases, the primary user interface of thecomputing device 104 may be a microphone and speaker.

The network 105 can include or constitute a display network, e.g., asubset of information resources available on the internet that areassociated with a content placement or search engine results system, orthat are eligible to include third party content items as part of acontent item placement campaign. The network 105 can be used by the dataprocessing system 102 to access information resources such as web pages,web sites, domain names, or uniform resource locators that can bepresented, output, rendered, or displayed by the client computing device104. For example, via the network 105 a user of the client computingdevice 104 can access information or data provided by the contentprovider computing device 106 or the chatbot provider computing device108.

The network 105 may be any type or form of network and may include anyof the following: a point-to-point network, a broadcast network, a widearea network, a local area network, a telecommunications network, a datacommunication network, a computer network, an ATM (Asynchronous TransferMode) network, a SONET (Synchronous Optical Network) network, a SDH(Synchronous Digital Hierarchy) network, a wireless network and awireline network. The network 105 may include a wireless link, such asan infrared channel or satellite band. The topology of the network 105may include a bus, star, or ring network topology. The network mayinclude mobile telephone networks using any protocol or protocols usedto communicate among mobile devices, including advanced mobile phoneprotocol (“AMPS”), time division multiple access (“TDMA”), code-divisionmultiple access (“CDMA”), global system for mobile communication(“GSM”), general packet radio services (“GPRS”) or universal mobiletelecommunications system (“UMTS”). Different types of data may betransmitted via different protocols, or the same types of data may betransmitted via different protocols.

The system 100 can include at least one data processing system 102. Thedata processing system 102 can include at least one logic device such asa computing device having a processor to communicate via the network105, for example with the computing device 104, the content providerdevice 106 (content provider computing device 106), or the chatbotprovider device 108 (or chatbot provider 108). The data processingsystem 102 can include at least one computation resource, server,processor or memory. For example, the data processing system 102 caninclude a plurality of computation resources or servers located in atleast one data center. The data processing system 102 can includemultiple, logically-grouped servers and facilitate distributed computingtechniques. The logical group of servers may be referred to as a datacenter, server farm or a machine farm. The servers can also begeographically dispersed. A data center or machine farm may beadministered as a single entity, or the machine farm can include aplurality of machine farms. The servers within each machine farm can beheterogeneous—one or more of the servers or machines can operateaccording to one or more type of operating system platform.

Servers in the machine farm can be stored in high-density rack systems,along with associated storage systems, and located in an enterprise datacenter. For example, consolidating the servers in this way may improvesystem manageability, data security, the physical security of thesystem, and system performance by locating servers and high performancestorage systems on localized high performance networks. Centralizationof all or some of the data processing system 102 components, includingservers and storage systems, and coupling them with advanced systemmanagement tools allows more efficient use of server resources, whichsaves power and processing requirements and reduces bandwidth usage.

The system 100 can include, access, or otherwise interact with at leastone chatbot provider device 108. The chatbot provider device 108 caninclude at least one logic device such as a computing device having aprocessor to communicate via the network 105, for example with thecomputing device 104, the data processing system 102, or the contentprovider computing device 106. The chatbot provider device 108 caninclude at least one computation resource, server, processor or memory.For example, chatbot provider device 108 can include a plurality ofcomputation resources or servers located in at least one data center.The chatbot provider device 108 can include one or more component orfunctionality of the data processing system 102.

The chatbot provider device 108 can include or refer to a chatbotdeveloper, such as an entity that designs, develops, manages, ormaintains computer programs that form or provide one or more chatbots. Achatbot can include a computer program that conducts a conversation viaauditory, image, or textual methods. The chatbot can be designed tosimulate how a human would behave as a conversational partner. Chatbotscan be used in dialog systems for customer service or informationacquisition. Chatbots can include or use natural language processingsystems (e.g., natural language processor component 112). The chatbotcan scan for keywords within an input, and then pull a reply with themost matching keywords, or the most similar wording pattern, from adatabase. The chatbot can be programmed with procedures that utilizepattern-matching to lookup predefined dialog data structures. Thechatbot can be programmed with natural language processing techniques toidentify a grammar and syntax of input, tokenize input, or otherwiseprocess the input to determine a response.

The content provider computing device 106 can provide audio basedcontent items for display by the client computing device 104 as an audiooutput content item. The content item can be or include a digitalcomponent. The content item can be or include a digital object. Thecontent item can include a brand name or company name of a good orservice. The content item can be configured for a parametrically driventext to speech technique. The content item can be configured for atext-to-speech (TTS) implementations that converts normal language textinto speech. The content item can be input to an application programminginterface that utilizes a speech-synthesis capability to synthesize textinto natural-sounding speech in a variety of languages, accents, andvoices. The content item can be coded as plain text or a speechsynthesis markup language (SSML). SSML can include parameters that canbe set to control aspects of speech, such as pronunciation, volume,pitch, or rate that can form an acoustic fingerprint or native voice.

For example, a chatbot can identify a dialog data structure such as “TheIngredients for Chicken Wings are: 1 cup brown sugar, 1 can<placeholder> cola, 2 medium onions, and 2 cloves garlic.” The contentprovider computing device 106 can provide a content item to be insertedinto the <placeholder> in the dialog data structure, such as a brandname of cola. The content provider computing device 106 can providecontent selection criteria for the content item, such as a value,keyword, concept, or other metadata or information to facilitate acontent selection process. The content provider computing device 106 canalso provide audio based content items (or other content items) to thedata processing system 102 where they can be stored in the datarepository 124. The data processing system 102 can select the audiocontent items (or content items configured for a parametrically driventext, image, or video to speech technique) and provide (or instruct thecontent provider computing device 106 to provide) the audio contentitems to the client computing device 104. The audio based content itemscan be exclusively audio or can be combined with text, image, or videodata.

The content provider computing device 106 can provide the content itemto the data processing system 102 for storage in the data repository 124in the content data data structure 130. The data processing system 102can retrieve the content item responsive to a request for content orotherwise determining to provide the content item.

The chatbot provider device 108 can include, interface, or otherwisecommunicate with at least one chatbot provider natural languageprocessor component 142 and a chatbot provider interface 144. Thechatbot provider computing device 108 can include at least one chatbotprovider natural language processor (NLP) component 142 and at least onechatbot provider interface 144. The chatbot provider NLP component 142(or other components such of the chatbot provider computing device 108or chatbot platform 146) can engage with the client computing device 104(via the data processing system 102 or bypassing the data processingsystem 102) to create a back-and-forth real-time voice or audio basedconversation (e.g., a session) between the client computing device 104and the chatbot provider computing device 108. The chatbot provider NLP142 can include one or more function or feature as the NLP component 112of the data processing system 102. For example, the chatbot providerinterface 144 can receive or provide data messages to the interface 110of the data processing system 102. The chatbot provider computing device108 and the content provider computing device 106 can be associated withthe same entity. For example, the content provider computing device 106can create, store, or make available content items for a chatbot, andthe chatbot provider computing device 108 can establish a session withthe client computing device 106 to communicate via a chatbot via theclient computing device 104. The data processing system 102, via theinterface 110, chatbot component 114, session handler component 120 orother components can also establish the session with the clientcomputing device 104, including or bypassing the chatbot providercomputing device 104 or the third-party chatbot platform 146.

The third-party chatbot platform 146 can refer to one or more servers ofan entity that is different from the entity that administers or providesthe data processing system 102. The third-party chatbot platform 146 canreceive computer programs for a chatbot from the chatbot provider device108. The third-party chatbot platform 146 can provide natural languageprocessing and other functions. The third-party chatbot platform 146 caninterface or communicate with the computing device 104 to provide thechatbot functionality. For example, third-party chatbot platform 146 canexecute or run the chatbot provided by the chatbot provider device 108in order to engage in a conversion with a user of the computing device104. The third-party chatbot platform 146 can execute on a server removefrom the data processing system 102 and computing device 104. In somecases, the third-party chatbot platform 146 can execute at leastpartially on the computing device 104 (e.g., as part of pre-processor140).

The computing device 104 can include, interface, or otherwisecommunicate with at least one sensor 134, transducer 136, audio driver138, or pre-processor 140. The sensor 134 can include, for example, acamera, an ambient light sensor, proximity sensor, temperature sensor,accelerometer, gyroscope, motion detector, GPS sensor, location sensor,microphone, video, image detection, or touch sensor. The transducer 136can include or be part of a speaker or a microphone. The audio driver138 can provide a software interface to the hardware transducer 136. Theaudio driver can execute the audio file or other instructions providedby the data processing system 102 to control the transducer 136 togenerate a corresponding acoustic wave or sound wave. The pre-processor140 can detect a keyword and perform an action based on the keyword. Thepre-processor 140 can filter out one or more terms or modify the termsprior to transmitting the terms to the data processing system 102 forfurther processing. The pre-processor 140 can convert the analog audiosignals detected by the microphone into a digital audio signal, andtransmit one or more data packets carrying the digital audio signal tothe data processing system 102 via the network 105. In some cases, thepre-processor 140 can transmit data packets carrying some or all of theinput audio signal responsive to detecting an instruction to performsuch transmission. The instruction can include, for example, a triggerkeyword or other keyword or approval to transmit data packets comprisingthe input audio signal to the data processing system 102.

The client computing device 104 can be associated with an end user thatenters voice queries as audio input into the client computing device 104(via the sensor 134) and receives audio output in the form of acomputer-generated voice that can be provided from the data processingsystem 102 (or the content provider computing device 106 or the chatbotprovider computing device 108) to the client computing device 104,output from the transducer 136 (e.g., a speaker). The computer-generatedvoice can include recordings from a real person or computer generatedlanguage.

The client computing device 104 can be associated with an end user thatprovides image or video that can indicate queries as input into theclient computing device 104 (via the sensor 134), and receives audiooutput in the form of a computer-generated voice that can be providedfrom the data processing system 102 (or the content provider computingdevice 106 or the chatbot provider computing device 108) to the clientcomputing device 104, output from the transducer 136 (e.g., a speaker).The input detected by the one or more sensors 134 can include one ormore of audio input (e.g., acoustic signal), visual input (e.g., imageor video data), motion input or other input. The input (e.g., the one ormore of audio, image, visual, or motion input) to the computing device104 can be converted to a digital file and provided to the dataprocessing system 102 for further processing or to generate actions. Forexample, the input (e.g., the one or more of audio, image, visual, ormotion input) to the computing device 104 can trigger the selection of acomputer program comprising a chatbot, trigger the generation of a queryto be input to the chatbot, and the chatbot can provide output that isresponsive to the query generated or corresponding to the input (e.g.,the one or more of audio, image, visual, or motion input) to thecomputing device 104.

The data repository 124 can include one or more local or distributeddatabases, and can include a database management system. The datarepository 124 can include computer data storage or memory and can storeone or more profiles 126, one or more indexes 128, content data 130, orchatbot data 132 among other data. The profile 126 can includeinformation about the computing device 104 or an account associated withthe computing device 104. The profile 126 can include historical networkactivity associated with the computing device 104, identifiers ofchatbots utilized by computing device 104, a configuration of thecomputing device 104, device functionality, preferences, or otherinformation associated with the computing device 104 that can facilitatecontent selection. The index 128 can map previously selected contentitems to a session identifier, computing device identifier, dialog datastructure identifier to facilitate reuse of the content item. Thecontent data 130 can include content items for audio output orassociated metadata, as well as input audio messages that can be part ofone or more communication sessions with the client computing device 104.The chatbot data 132 can include identifiers for chatbots, informationabout types of chatbots (e.g., category, restrictions, or topics).

The data processing system 102 can include a content placement systemhaving at least one computation resource or server. The data processingsystem 102 can include, interface, or otherwise communicate with atleast one interface 110. The data processing system 102 can include,interface, or otherwise communicate with at least one natural languageprocessor component 112. The data processing system 102 can include,interface, or otherwise communicate with at least one chatbot component114. The data processing system 102 can include, interface, or otherwisecommunicate with at least one placeholder generation component 116. Thedata processing system 102 can include, interface, or otherwisecommunicate with at least one content selector component 118. The dataprocessing system 102 can include, interface, or otherwise communicatewith at least one session handler component 118. The data processingsystem 102 can include, interface, or otherwise communicate with atleast one validation component 122. The data processing system 102 caninclude, interface, or otherwise communicate with at least one datarepository 124. The at least one data repository 124 can include orstore, in one or more data structures or databases, profiles 126,indexes 128, content data 130, or chatbot data 132. Content data 130 caninclude, for example, content campaign information, content groups,content selection criteria, content item objects or other informationprovided by a content provider computing device 106 or obtained ordetermined by the data processing system to facilitate contentselection. The content data 130 can include, for example, historicalperformance of a content campaign.

The interface 110, natural language processor component 112, chatbotcomponent 114, placeholder generation component 116, content selectorcomponent 118, session handler component 120, or validation component122 can each include at least one processing unit or other logic devicesuch as programmable logic array engine, or module configured tocommunicate with the database repository or database 124. The interface110, natural language processor component 112, chatbot component 114,placeholder generation component 116, content selector component 118,session handler component 120, validation component 122 and datarepository 124 can be separate components, a single component, or partof the data processing system 102. The system 100 and its components,such as a data processing system 102, can include hardware elements,such as one or more processors, logic devices, or circuits.

The data processing system 102 can obtain anonymous computer networkactivity information associated with a plurality of computing devices104. A user of a computing device 104 can affirmatively authorize thedata processing system 102 to obtain network activity informationcorresponding to the user's computing device 104. For example, the dataprocessing system 102 can prompt the user of the computing device 104for consent to obtain one or more types of network activity information.The identity of the user of the computing device 104 can remainanonymous and the computing device 104 can be associated with a uniqueidentifier (e.g., a unique identifier for the user or the computingdevice provided by the data processing system or a user of the computingdevice). The data processing system can associate each observation witha corresponding unique identifier.

A content provider computing device 106 can establish an electroniccontent campaign. The electronic content campaign can be stored ascontent data 130 in data repository 124. An electronic content campaigncan refer to one or more content groups that correspond to a commontheme. A content campaign can include a hierarchical data structure thatincludes content groups, content item data objects (e.g., digitalcomponents or digital objects), and content selection criteria. Tocreate a content campaign, content provider computing device 106 canspecify values for campaign level parameters of the content campaign.The campaign level parameters can include, for example, a campaign name,a preferred content network for placing content item objects, a value ofresources to be used for the content campaign, start and end dates forthe content campaign, a duration for the content campaign, a schedulefor content item object placements, language, geographical locations,type of computing devices on which to provide content item objects. Insome cases, an impression can refer to when a content item object isfetched from its source (e.g., data processing system 102 or contentprovider computing device 106), and is countable. In some cases, due tothe possibility of click fraud, robotic activity can be filtered andexcluded, as an impression. Thus, in some cases, an impression can referto a measurement of responses from a Web server to a page request from abrowser, which is filtered from robotic activity and error codes, and isrecorded at a point as close as possible to opportunity to render thecontent item object for display on the computing device 104. In somecases, an impression can refer to a viewable or audible impression;e.g., the content item object or digital component is at least partially(e.g., 20%, 30%, 30%, 40%, 50%, 60%, 70%, or more) viewable on a displaydevice of the client computing device 104, or audible via a speaker 136of the computing device 104. A click or selection can refer to a userinteraction with the content item object, such as a voice response to anaudible impression, a mouse-click, touch interaction, gesture, shake,audio interaction, or keyboard click. A conversion can refer to a usertaking a desired action with respect to the content item objection;e.g., purchasing a product or service, completing a survey, visiting aphysical store corresponding to the content item, or completing anelectronic transaction.

The content provider computing device 106 can further establish one ormore content groups for a content campaign. A content group includes oneor more content item objects and corresponding content selectioncriteria, such as keywords, words, terms, phrases, geographic locations,type of computing device, time of day, interest, topic, or vertical.Content groups under the same content campaign can share the samecampaign level parameters, but may have tailored specifications forcontent group level parameters, such as keywords, negative keywords(e.g., that block placement of the content item in the presence of thenegative keyword on main content), bids for keywords, or parametersassociated with the bid or content campaign.

To create a new content group, the content provider computing device 106can provide values for the content group level parameters of the contentgroup. The content group level parameters include, for example, acontent group name or content group theme, and bids for differentcontent placement opportunities (e.g., automatic placement or managedplacement) or outcomes (e.g., clicks, impressions, or conversions). Acontent group name or content group theme can be one or more terms thatthe content provider computing device 106 can use to capture a topic orsubject matter for which content item objects of the content group is tobe selected for display. For example, a food and beverage company cancreate a different content group for each brand of food or beverage itcarries, and may further create a different content group for each modelof vehicle it carries. Examples of the content group themes that thefood and beverage company can use include, for example, “Brand A cola”,“Brand B ginger ale,” “Brand C orange juice,” “Brand D sports drink,” or“Brand E purified water.” An example content campaign theme can be“soda” and include content groups for both “Brand A cola” and “Brand Bginger ale”, for example. The content item (or content item object ordigital component) can include “Brand A”, “Brand B”, “Brand C”, “BrandD” or “Brand E”. The content item object or digital component can referto the content item configured for a parametrically driven text tospeech technique.

The content provider computing device 106 can provide one or morekeywords and content item objects to each content group. Keywords caninclude terms that are relevant to the product or services of associatedwith or identified by the content item objects. A keyword can includeone or more terms or phrases. For example, the food and beverage companycan include “soda,” “cola,” “soft drink,” as keywords for a contentgroup or content campaign that can be descriptive of the goods orservices the brand provides. In some cases, negative keywords can bespecified by the content provider to avoid, prevent, block, or disablecontent placement on certain terms or keywords. The content provider canspecify a type of matching, such as exact match, phrase match, or broadmatch, used to select content item objects.

The content provider computing device 106 can provide one or morekeywords to be used by the data processing system 102 to select acontent item object provided by the content provider computing device106. The content provider computing device 106 can identify one or morekeywords to bid on, and further provide bid amounts for variouskeywords. The content provider computing device 106 can provideadditional content selection criteria to be used by the data processingsystem 102 to select content item objects. Multiple content providers106 can bid on the same or different keywords, and the data processingsystem 102 can run a content selection process or ad auction responsiveto receiving an indication of a keyword of an electronic message.

The content provider computing device 106 can provide one or morecontent item objects for selection by the data processing system 102.The data processing system 102 (e.g., via content selector component118) can select the content item objects when a content placementopportunity becomes available that matches the resource allocation,content schedule, maximum bids, keywords, and other selection criteriaspecified for the content group. Different types of content item objectscan be included in a content group, such as a voice content item, audiocontent item, a text content item, an image content item, video contentitem, multimedia content item, or content item link. Upon selecting acontent item, the data processing system 102 can transmit the contentitem object for rendering on a computing device 104 or display device ofthe computing device 104. Rendering can include displaying the contentitem on a display device, or playing the content item via a speaker ofthe computing device 104. The data processing system 102 can provideinstructions to a computing device 104 or chatbot component 114, orthird-party chatbot platform 146 to present the content item object. Thedata processing system 102 can instruct the computing device 104, or anaudio driver 138 of the computing device 104, to generate audio signalsor acoustic waves.

The data processing system 102 can include an interface component 110designed, configured, constructed, or operational to receive andtransmit information using, for example, data packets. The interface 110can receive and transmit information using one or more protocols, suchas a network protocol. The interface 110 can include a hardwareinterface, software interface, wired interface, or wireless interface.The interface 110 can facilitate translating or formatting data from oneformat to another format. For example, the interface 110 can include anapplication programming interface that includes definitions forcommunicating between various components, such as software components.

The data processing system 102 can include an application, script orprogram installed at the client computing device 104, such as anapplication to communicate input audio signals to the interface 110 ofthe data processing system 102 and to drive components of the clientcomputing device to render output audio signals. The data processingsystem 102 can receive data packets, a digital file, or other signalthat includes or identifies an audio input signal. The computing device104 can detect the audio signal via the transducer 136, and convert theanalog audio signal to a digital file via an analog-to-digitalconverter. For example, the audio driver 138 can include ananalog-to-digital converter component.

The data processing system 102 can execute or run the NLP component 112to receive or obtain the digital file comprising the audio signal andparse the audio signal. For example, the NLP component 112 can providefor interactions between a human and a computer. The NLP component 112can be configured with techniques for understanding natural language andallowing the data processing system 102 to derive meaning from human ornatural language input. The NLP component 112 can include or beconfigured with technique based on machine learning, such as statisticalmachine learning. The NLP component 112 can utilize decision trees,statistical models, or probabilistic models to parse the input audiosignal. The NLP component 112 can perform, for example, functions suchas named entity recognition (e.g., given a stream of text, determinewhich items in the text map to proper names, such as people or places,and what the type of each such name is, such as person, location, ororganization), natural language generation (e.g., convert informationfrom computer databases or semantic intents into understandable humanlanguage), natural language understanding (e.g., convert text into moreformal representations such as first-order logic structures that acomputer module can manipulate), machine translation (e.g.,automatically translate text from one human language to another),morphological segmentation (e.g., separating words into individualmorphemes and identify the class of the morphemes, which can bechallenging based on the complexity of the morphology or structure ofthe words of the language being considered), question answering (e.g.,determining an answer to a human-language question, which can bespecific or open-ended), semantic processing (e.g., processing that canoccur after identifying a word and encoding its meaning in order torelate the identified word to other words with similar meanings).

The NLP component 112 converts the audio input signal into recognizedtext by comparing the input signal against a stored, representative setof audio waveforms (e.g., in the data repository 124) and choosing theclosest matches. The set of audio waveforms can be stored in datarepository 124 or other database accessible to the data processingsystem 102. The representative waveforms are generated across a largeset of users, and then may be augmented with speech samples from theuser. After the audio signal is converted into recognized text, the NLPcomponent 112 matches the text to words that are associated, for examplevia training across users or through manual specification, with actionsthat the data processing system 102 can serve. The NLP component 112 canconvert image or video input to text or digital files. The NLP component112 can process, analyze or interpret image or video input to performactions, generate requests, or select or identify data structures.

The audio input signal can be detected by the sensor 134 or transducer136 (e.g., a microphone) of the client computing device 104. Via thetransducer 136, the audio driver 138, or other components the clientcomputing device 104 can provide the audio input signal to the dataprocessing system 102 (e.g., via the network 105) where it can bereceived (e.g., by the interface 110) as a digital file or digitalformat and provided to the NLP component 112 or stored in the datarepository 124. In some cases, the data processing system 102 canreceive image or video input signals, in addition to, or instead of,input acoustic signals. The data processing system 102 can process theimage or video input signals using, for example, image interpretationtechniques, computer vision, a machine learning engine, or othertechniques to recognize or interpret the image or video to convert theimage or video to a digital file. The one or more image interpretationtechniques, computer vision techniques, machine learning techniques canbe collectively referred to as imaging techniques. The data processingsystem 102 (e.g., the NLP component 112) can be configured with theimaging techniques, in addition to, or instead of, audio processingtechniques.

The NLP component 112 can obtain the input audio signal. From the inputaudio signal, the NLP component 112 can identify at least one request orat least one trigger keyword corresponding to the request. The requestcan indicate intent or subject matter of the input audio signal. Thetrigger keyword can indicate a type of action likely to be taken. Forexample, the NLP component 112 can parse the input audio signal toidentify at least one request to leave home for the evening to attenddinner and a movie. The trigger keyword can include at least one word,phrase, root or partial word, or derivative indicating an action to betaken. For example, the trigger keyword “go” or “to go to” from theinput audio signal can indicate a need for transport. In this example,the input audio signal (or the identified request) does not directlyexpress an intent for transport, however the trigger keyword indicatesthat transport is an ancillary action to at least one other action thatis indicated by the request.

The NLP component 112 can parse the input audio signal to identify,determine, retrieve, or otherwise obtain the request and the triggerkeyword. For instance, the NLP component 112 can apply a semanticprocessing technique to the input audio signal to identify the triggerkeyword or the request. The NLP component 112 can apply the semanticprocessing technique to the input audio signal to identify a triggerphrase that includes one or more trigger keywords, such as a firsttrigger keyword and a second trigger keyword. For example, the inputaudio signal can include the sentence “I need a recipe for chickenwings.” The NLP component 112 can apply a semantic processing technique,or other natural language processing technique, to the data packetscomprising the sentence to identify trigger phrases “need” “recipe” and“chicken wings”. The NLP component 112 can further identify multipletrigger keywords, such as need, recipe, and chicken wings. For example,the NLP component 112 can determine that the trigger phrase includes thetrigger keyword and a second trigger keyword.

The NLP component 112 can filter the input audio signal to identify thetrigger keyword. For example, the data packets carrying the input audiosignal can include “It would be great if I could get help with a recipefor chicken wings”, in which case the NLP component 112 can filter outone or more terms as follows: “it”, “would”, “be”, “great”, “if”, “I”,“could”, “get”, or “help”. By filtering out these terms, the NLPcomponent 112 may more accurately and reliably identify the triggerkeywords, such as “recipe for chicken wings” and determine that this isa request to launch a recipe chatbot.

In some cases, the NLP component can determine that the data packetscarrying the input audio signal includes one or more requests. Forexample, the input audio signal can include the sentence “I need somehelp with making chicken wings and movie show times.” The NLP component112 can determine this is a request for a recipe for chicken wings andmovie show times. The NLP component 112 can determine this is a singlerequest for a chatbot that can provide both recipes and movie times. TheNLP component 112 can determine that this is two requests; a firstrequest for a chatbot that provides recipes, and a second request for achatbot that provides movie times. In some cases, the NLP component 112can combine the multiple determined requests into a single request, andtransmit the single request to a chatbot component 114 or third-partychatbot platform 146. In some cases, the NLP component 112 can transmitthe individual requests to respective chatbot provider devices 108, orseparately transmit both requests to the same chatbot provider device108.

Thus, the data processing system 102 can receive a digital filecorresponding to a first acoustic signal carrying voice content detectedby a transducer 136 of the computing device 104. The first acousticsignal can be converted to the digital file by an analog to digitalconverter (e.g., audio driver 138) of the computing device 104. The dataprocessing system 102 can parse the digital file to select a computerprogram comprising a chatbot. For example, the data processing system102 can include a chatbot component 114 designed and constructed toselect, responsive to the digital file, a computer program that includesa chatbot for execution by the data processing system 102 or computingdevice 104 or third-party chatbot platform 146.

The chatbot component 114 can identify keywords, tokens, terms,concepts, or other information in the digital file. The chatbotcomponent 114 can utilize the natural language processor component 112to identify keywords, tokens, terms, concepts, or other information inthe digital file. The natural language processor component 112 canprovide the parsed keyword, token, term or concept to the chatbotcomponent 114. The chatbot component 114 can select a chatbot that isresponsive to a keyword or concept of the digital file.

For example, the data processing system 102 can determine that the firstdigital file includes a request for a recipe chatbot. The chatbotcomponent 114 can perform a lookup in a chatbot data structure 132 toidentify a chatbot that can provide recipes. For example, the chatbotdata structure 132 can include keywords or other information thatdescribes, for each chatbot, the goods, service or function the chatbotcan provide. The chatbot component 114 can use the identifier determinedvia the chatbot data structure 132 to launch, initiate, execute orotherwise activate the corresponding chatbot. In some cases, theidentifier can include or be associated with a filename or file path,pointer, web address, internet protocol address, uniform resourcelocator, or other identifying information for the chatbot. For example,the data processing system 102 can determine the recipe chatbot isprovided via the third-party chatbot platform 146, and instruct thethird-party chatbot platform 146 to launch the recipe chatbot and engagewith the computing device 104 either directly or via the data processingsystem 102 (e.g., via the chatbot component 114).

Prior to launching or causing the launch or execution of the chatbot,the data processing system 102 can determine whether the computingdevice 104 is authorized to access the chatbot. The data processingsystem 102 (e.g., via chatbot component 114) can perform a lookup in thedata repository 124 (e.g., profile data structure 126) with theidentifier of the computing device 104 to determine if the computingdevice 104 is authorized to access the computer program comprising thechatbot. Authorization can be based on a subscription, plan,restriction, resource requirement, versioning, or device functionality.For example, the data processing system 102 can grant the computingdevice 104 access to the chatbot if the computing device 104 isconfigured with a predefined version of an operating system. In anotherexample, the data processing system 102 can grant the computing device104 access to the chatbot if the computing device 104 is associated witha valid account or profile. In some cases, if the data processing system102 determines that the computing device 102 is not authorized to accessthe chatbot, the data processing system 102 can terminate the thread,prompt the user, or identify another chatbot the computing device 104 isauthorized to access. Thus, the data processing system 102 can selectthe chatbot responsive to the determination that the computing device104 is authorized to access to the chatbot.

The interface 110 can launch the chatbot itself, or transmit theinstruction to a third-party chatbot platform 146 to cause thethird-party chatbot platform 146 to invoke a conversational applicationprogramming interface associated with the chatbot (e.g., NLP component142) and establish a communication session between the data processingsystem 102 or the third-party chatbot platform 146 and the clientcomputing device 104. Responsive to establishing the communicationsession between the data processing system 102 or the third-partychatbot platform 146 and the client computing device 104, the dataprocessing system 102 or third-party chatbot platform 146 can transmitdata packets directly to the client computing device 104 via network105. In some cases, the third-party chatbot platform 146 can transmitdata packets to the client computing device 104 via data processingsystem 102 and network 105.

In some cases, the chatbot provider device 108, chatbot or third-partychatbot platform 146 can execute at least a portion of theconversational API 142. For example, the third-party chatbot platform146 can handle certain aspects of the communication session or types ofqueries. The third-party chatbot platform 146 may leverage the NLPcomponent 112 executed by the data processing system 102 to facilitateprocessing the audio signals associated with the communication sessionand generating responses to queries. In some cases, the data processingsystem 102 can include the conversational API 142 configured forthird-party chatbot platform 146. In some cases, the data processingsystem routes data packets between the client computing device and thethird-party provider device to establish the communication session. Thedata processing system 102 can receive, from the third-party chatbotplatform 146, an indication that the third-party device established thecommunication session with the client device 104. The indication caninclude an identifier of the client computing device 104, timestampcorresponding to when the communication session was established, orother information associated with the communication session, such as thedata structure associated with the communication session.

The conversational API can be a second NLP that includes one or morecomponent or function of the first NLP 112. The second NLP 142 caninteract or leverage the first NLP 112. In some cases, the system 100can include a single NLP 112 executed by the data processing system 102.The single NLP 112 can support both the data processing system 102 andthe chatbot. In some cases, interface 110 generates or constructs a datastructure to facilitate performing a service, and the conversational APIgenerates responses or queries to further a communication session withan end user or obtain additional information to improve or enhance theend user's experience or performance of the service.

The computer program comprising the chatbot can execute on the dataprocessing system 102, chatbot provider device 108, or third-partychatbot platform 146. The chatbot can receive and process one or moredigital files or portions of one or more digital files to determine aresponse. For example, the chatbot can execute as the chatbot component114 on the data processing system 102.

The chatbot, upon execution, can identify a dialog data structure thatis responsive to the digital file. For example, the digital file cancorrespond to a voice input of “I need a recipe for chicken wings.” Thechatbot, such as a recipe chatbot, can identify a dialog data structureresponsive to the query using a natural language processing technique,search engine technique, pattern matching technique, or semanticanalysis technique. For example, the dialog data structure can includeingredients for chicken wings. The dialog data structure can include aplaceholder field. The placeholder field can be populated with a contentitem. The placeholder field can serve as a tag or indication thattriggers a request for content.

The developer of the chatbot can include the placeholder field as partof the computer program for the chatbot or the response for the query.The developer of the chatbot can program the placeholder field using anapplication programming interface, script, tag, markup language or othermechanism that allows the chatbot to identify the placeholder field,request content, and populate the placeholder field with a selectedcontent item. The placeholder field can be associated with metadata thatprovides content selection criteria that can be used to select a contentitem that is relevant to the dialog data structure and appropriate forinsertion in the placeholder field. For example, if the dialog datastructure is a list of ingredients, and the placeholder field precedesand modifies the term “cola”, then the metadata or content selectioncriteria can indicate to select a content item that includes a brandname or company that sells cola.

In some cases, the dialog data structure may not include a placeholderfield. The data processing system 102 can receive or intercept theidentified dialog data structure. The data processing system 102 caninclude a placeholder generation component 116 designed and constructedto identify a portion of the dialog data structure at which to insertthe placeholder field. The placeholder generation component 116 can useor interface with the natural language processing component 112 toprocess the dialog data structure and identify a portion at which toinsert the placeholder field. The placeholder generation component 116can identify the portion based on keywords or terms in the dialog datastructure. The placeholder generation component 116 can identify theportion based on available content items. For example, the placeholdergeneration component 116 can identify the keyword “cola” in the dialogdata structure, and further determine that there is placeholder fieldpreceding the term “cola”. The placeholder generation component 116 candetermine that content data data structure 130 includes content itemsassociated with keyword “cola”. The placeholder generation component 116can determine to initiate a content selection process for keyword“cola”, and insert the selected content item adjacent to the term “cola”in the dialog data structure.

The placeholder generation component 116 can determine whether and whereto insert the placeholder field, or content item thereof, using thenatural language processing technique (e.g., via natural languageprocessor component 112). For example, the placeholder generationcomponent 116 can use the NLP component 112 to identify a grammar andsyntax of the dialog data structure as well as keywords of the datastructure. Based on the keyword, grammar, and syntax, the placeholdergeneration component 116 can determine where to insert the placeholderfield. Grammar can refer to a set of rules in a given language. Syntaxcan refer to the structure of the sentence. Based on the grammar andsyntax of the dialog data structure, the placeholder generationcomponent 116 can determine an appropriate position for the placeholderfield. For example, placeholder generation component 116 can determineto place the placeholder field adjacent to and before a noun in thedialog data structure. The placeholder generation component 116 candetermine to place the placeholder field adjacent to and before a nounin the dialog data structure that is located at the beginning, middle orend of the dialog data structure. The placeholder generation component116 may determine not to position the placeholder field adjacent to averb, pronoun, adjective, or adverb. In some cases, the placeholdergeneration component 116 may determine not to insert a placeholder fieldin the dialog data structure. For example, the only noun in the dialogdata structure may be the first term in the dialog data structure, andthe placeholder generation component 116 can be configured to not inserta content item as the first term in the dialog data structure.

Thus, the data processing system 102 can automatically insert aplaceholder field in a dialog data structure, and populate theplaceholder field with a content item. By automatically generating theplaceholder field, the chatbot computer program may occupy less memoryor have less complicated and error prone code because the developer maynot include placeholder fields in each dialog data structure.

The chatbot, upon identifying the placeholder field, can transmit arequest for content. In some cases, the placeholder generation component116, upon determining to insert the placeholder field, can trigger thecontent selection process via the content selector component 118 withoutreturning the dialog data structure to the chatbot.

The data processing system 102 can include, execute, or otherwisecommunicate with a content selector component 118 to receive the triggerkeyword identified by the natural language processor and select, basedon the trigger keyword, a content item via a real-time content selectionprocess. The content selection process can refer to, or include,selecting sponsored content item objects provided by third party contentproviders 106. The real-time content selection process can include aservice in which content items provided by multiple content providersare parsed, processed, weighted, or matched in order to select one ormore content items to provide to the computing device 104. The contentselector component 118 can perform the content selection process inreal-time. Performing the content selection process in real-time canrefer to performing the content selection process responsive to therequest for content received via the client computing device 104. Thereal-time content selection process can be performed (e.g., initiated orcompleted) within a time interval of receiving the request (e.g., 5seconds, 10 seconds, 20 seconds, 30 seconds, 1 minute, 2 minutes, 3minutes, 5 minutes, 10 minutes, or 20 minutes). The real-time contentselection process can be performed during a communication session withthe client computing device 104, or within a time interval after thecommunication session is terminated.

For example, the data processing system 102 can include a contentselector component 118 designed, constructed, configured or operationalto select content item objects. The content selector component 118 canidentify, analyze, or recognize voice, audio, terms, characters, text,symbols, or images of the candidate content items using an imageprocessing technique, character recognition technique, natural languageprocessing technique, or database lookup. The candidate content itemscan include metadata indicative of the subject matter of the candidatecontent items, in which case the content selector component 118 canprocess the metadata to determine whether the subject matter of thecandidate content item corresponds to the input audio signal.

Content providers 106 can provide additional indicators when setting upa content campaign that includes content items. The content providercomputing device 106 can provide information at the content campaign orcontent group level that the content selector component 118 can identifyby performing a lookup using information about the candidate contentitem. For example, the candidate content item may include a uniqueidentifier, which may map to a content group, content campaign, orcontent provider. The content selector component 118 can determine,based on information stored in content campaign data structure in datarepository 124, information about the content provider computing device106.

The data processing system 102 can receive a request for content forprovision via a computing device 104. The request can include selectioncriteria of the request, such as the device type, location, and akeyword associated with the request. The request can include the dialogdata structure.

Responsive to the request, the data processing system 102 can select acontent item object from data repository 124 or a database associatedwith the content provider computing device 106, and provide the contentitem for presentation via the computing device 104 via network 105. Thecontent item object can be provided by a content provider device 108different from the chatbot provider device 108. The computing device 104can interact with the content item object. The computing device 104 canreceive an audio response to the content item. The computing device 104can receive an indication to select a hyperlink or other buttonassociated with the content item object that causes or allows thecomputing device 104 to identify content provider computing device 106,request a service from the content provider computing device 106,instruct the content provider computing device 106 to perform a service,transmit information to the content provider computing device 106, orotherwise identify a good or service associated with content providercomputing device 106.

The request for content can include content selection criteria, such asa format of the content, keywords, concepts, profile information, orother information that can facilitate content selection. The contentselector component 118 can perform a real-time content selectionprocess. Real-time content selection can refer to performing the contentselection responsive to the request for content. The request for contentcan be generated, transmitted or otherwise provided after the chatbotidentifies the dialog data structure that is responsive to the voiceinput.

The content selector component 118 can select a content item thatincludes text, string, or characters that can be processed by a text tospeech system. The content selector component 118 can select a contentitem that is in a parameterized format configured for a parametricallydriven text to speech technique. In some cases, the dialog datastructure can be in SSML format or be configured with voice parameters.The data processing system 102 can configure the voice parameters of thecontent item to match the voice parameters of the dialog data structureidentified by the chatbot such that the content item can be presented tothe user of the computing device 104 with a native voice, image, oracoustic fingerprint (e.g., the content item has the same or similaracoustic properties as compared to the dialog data structure without thecontent item).

The content selector component 118 can select a content item that is ina parameterized format configured for text to speech instead of acontent item that is in an audio file format. For example, the contentselector component 118 may not select a content item in an audio file inan audio file format or audio coding format, such as .WAV, .AIFF, or.AU, because a content item already in an audio file format may not beconfigured for seamless insertion into the placeholder field of thedialog data structure identified by the chatbot computer program. Acontent item in an audio file format may have a different acousticfingerprint as compared to a native voice of the computing device or theacoustic fingerprint set for the chatbot. If the content item audio filehas a different acoustic fingerprint as compared to the native voice oracoustic fingerprint of the chatbot or the dialog data structure (e.g.,words are spoken at different rate, at a different frequency, differentpitch, different tone, different volume, or different accent), theninserting or integrating the content item audio file into theplaceholder field in the dialog data structure may not be seamless,smooth or continuous. For example, the content item audio file havingthe different acoustic fingerprint can cause awkward transitions orindication of disparity. Thus, by providing the content item configuredfor a text to speech technique in which the chatbot or computing devicecan play the content item in a manner that corresponds to the acousticfingerprint or native voice of the chatbot or computing device, the dataprocessing system 102 can facilitate providing the seamless modificationof chatbot computer program output.

The content selector component 118 can provide the selected content itemto the chatbot to cause the computing device to perform the text tospeech technique to generate an acoustic signal corresponding to thedialog data structure modified with the selected content item. In somecases, the data processing system 102 can transmit data packetscorresponding to the content item. The data processing system 102 cantransmit data packets corresponding to the dialog data structuremodified with the content item.

The data processing system 102 can include, execute, access, orotherwise communicate with a session handler component 120 to establisha session. The session handler component 120 can establish the sessionresponsive to the first digital file. For example, the session handlercomponent 120 can establish a communication session between the clientdevice 104 and the data processing system 102. The communication sessioncan refer to one or more data transmissions between the client device104 and the data processing system 102 that includes the digital filecorresponding to the input audio signal that is detected by a sensor 134of the client device 104, and the output signal transmitted by the dataprocessing system 102 to the client device 104. The data processingsystem 102 (e.g., via the session handler component 120) can establishthe communication session responsive to receiving the input audiosignal. The data processing system 102 can set a duration for thecommunication session. The data processing system 102 can set a timer ora counter for the duration set for the communication session. Responsiveto expiration of the timer, the data processing system 102 can terminatethe communication session.

The session handler component 120 can determine a break, pause, or endof the session based on one or more of a temporal threshold, a locationthreshold, or natural language processing. The temporal threshold caninclude, for example, a time interval such as 5 minutes, 10 minutes, 15minutes, 20 minutes, 30 minutes, 1 hour, 2 hours, 6 hours, or more. Thelocation threshold can refer to a distance between the location of thecomputing device 104 at the time the session is established, and acurrent location of the computing device 104. The distance threshold canbe 0.5 miles, 1 mile, 2 miles, 3 miles, 5 miles, 10 miles, 20 miles, 50miles or more. The temporal threshold and location threshold can bedynamic thresholds that vary based on time of day, geographic location,population density, historical profile information, type of chatbot, orother information associated with the session. For example, the temporalthreshold and distance may be shorter if the chatbot is related tofinding a coffee shop as compared to a chatbot that facilitates bookinga travel vacation because the process of booking a travel vacation canspan several days. The natural language processing can indicate a breakbased on a change in a topic or category of the conversation.

The communication session can refer to a network-based communicationsession in which the client device 104 provides authenticatinginformation or credentials to establish the session. In some cases, thecommunication session refers to a chatbot, topic or a context of audiosignals carried by data packets during the session. For example, a firstcommunication session can refer to audio signals transmitted between theclient device 104 and the data processing system 102 that are related to(e.g., include keywords, dialog data structures, chatbot, or contentitem objects) a recipe; and a second communication session can refer toaudio signals transmitted between the client device 104 and dataprocessing system 102 that are related to movie tickets. In thisexample, the data processing system 102 can determine that the contextof the audio signals is different (e.g., via the NLP component 112), andseparate the two sets of audio signals into different communicationsessions. The session handler 114 can terminate the first sessionrelated to the recipe responsive to identifying one or more audiosignals related to the movie tickets. Thus, the data processing system102 can initiate or establish the second session for the audio signalsrelated to the movie tickets responsive to detecting the context of theaudio signals.

The session handler component 120 be designed and constructed to allowreuse of a content item that was previously selected for provision witha dialog data structure. The session handler component 120 can prevent,block, disable, or cancel a second content selection process by thecontent selector component 118 responsive to the session handlercomponent 120 determining to reuse a previously selected content item.By avoiding a redundant content selection process, or otherwiseeliminating a content selection process, the session handler component120 can reduce processor and other computational resource utilization bythe data processing system 102.

The data processing system 102 can reuse or resurface the content itemduring a same session, or after determining a session break anddetermining to merge the subsequent session with the previous session inorder to resume the previous session. The data processing system 102 canresume a session after determining a break in the session. The dataprocessing system 102 can resume the session if a subsequent digitalfile, voice input, semantic input, or other information associated withthe subsequent digital file or voice input indicates that the currentvoice input corresponds to or relates to a previous session. Forexample, the session handler 120 can, via the natural language processorcomponent 112, compare a second digital file received after a sessionbreak with a first digital file received before the session break todetermine that they are related to one or more of a same topic,category, task flow, chatbot, computing device, or dialog datastructure. For example, the computing device 104 can invoke the samechatbot subsequent to the session break, and the data processing system102 can determine to resume the previous session.

To identify the content item to reuse, the data processing system 102can associate the previously selected content item with a value in theindex data structure 128. The value can be generated based on a firstidentifier of the chatbot associated with the selection of the contentitem, a second identifier for a first dialog data structure with whichthe content item was provided, and a third identifier for the computingdevice 104 associated with the provision of the content item. If thesession handler component 120 determines that a subsequent request forcontent item is associated with the same value, the session handlercomponent 120 can determine to reuse the previously selected contentitem.

For example, the selected content item “Brand A” may have been initiallyprovided, via computing device 104, with a dialog data structure thatincludes “ingredient: <placeholder> cola” as follows “ingredient: BrandA cola” as identified by chatbot. The first, second and thirdidentifiers can be alphanumeric identifiers, such as: first identifier:chatbot_123; second identifier: dialog_data_structure_123; thirdidentifier: computing_device_123. The identifier of the dialog datastructure can correspond to the topic, concept, category, or exactphrase of the dialog data structure. For example, the second identifierdialog_data_structure_123 can correspond to all dialog data structuresthat provide a chicken wing recipe. In another example, the secondidentifier dialog_data_structure_123 can correspond to all dialog datastructures that provide a chicken wing recipe with cola. In anotherexample, the second identifier dialog_data_structure_123 can correspondto all dialog data structures that provide a chicken wing recipe withcola with a placeholder field immediately preceding the term “cola”. Theindex value can be formed of these three tuples, or another numbertuple. The index value can be formed using a hash function that hashesthe tuple comprising the first, second and third identifiers. The indexvalue can refer to a hash value and be stored in a hash table (e.g.,index data structure 128). The index value can be numeric, alphanumeric,or contains symbols or other identifiers. The index value can correspondto a row and column in a table, or entries in a multi-dimensional table.The index value can correspond to a field in a data structure, such asindex_value{first_identifier, second identifier, third identifier}.

The data processing system 102 can associate the content item with thegenerated index value in the index data structure 128. The dataprocessing system 102 can associate, assign, or set additionalparameters or conditions for the association. The data processing system102 can set a duration for the association between the content item andthe generated index value, a geographic condition for the association,or a semantic condition. For example, if the duration of the sessionexceeds a time interval (e.g., 5 minutes, 10 minutes, 20 minutes, 30minutes, 1 hour, 2 hours, 6 hours, 12 hours, or more), the dataprocessing system 102 can terminate, break or end the associationbetween the content item and the index value. If the computing device104 has traveled or moved greater than a distance (e.g., 0.5 miles, 1mile, 2 miles, 3 miles, 5 miles, 10 miles, 20 miles, 30 miles, or more)from whether the computing device 104 was located when the associationbetween the content item and the index value was created, the dataprocessing system 102 can determine to terminate, break, or end theassociation.

The data processing system 102 can utilize the association between thecontent item and the index value to reuse the content item in a seconddialog data structure. For example, the data processing system 102 canreceive a second digital file corresponding to another acoustic signalcarrying voice content detected by the microphone on the computingdevice 104. The data processing system 102 can select, responsive to thesecond voice content of the second digital file, the computer programcomprising the chatbot corresponding to first identifier: chatbot_123.The chatbot can identify a second dialog data structure comprising asecond placeholder field. The chatbot can determine that the seconddialog data structure has a fourth identifier that corresponds to thesecond identifier of the first dialog data structure. For example, thesecond dialog data structure and the first dialog data structure canboth be related to a recipe for chicken wings and, therefore, correspondto the same identifier. Since the second dialog data structure isassociated with the same chatbot, computing device, and dialog datastructure identifier, the data processing system 102 can determine toreuse the previously selected content item for the placeholder field inthe second dialog data structure. The data processing system 102 cangenerate the index value with the identifiers from the second dialogdata structure, select the content item associated with the same indexvalue in the index data structure 128, and provide the same content itemwith the second dialog data structure to cause the chatbot or computingdevice to generate an acoustic signal corresponding to the second dialogdata structure modified with the same content item.

For example, the first dialog data structure with the content item canbe: “Ingredients for chicken wings includes Brand A cola”. The seconddialog data structure with the reused content item can be: “In acasserole, combine 1 can of the Brand A cola with the onions, garlic,and brown sugar.” Thus, the content item “Brand A” is reused in thesecond dialog data structure, thereby eliminating a second contentselection process and reducing computing resource utilization by thecontent selector component 118.

In some cases, the data processing system 102 can reuse the content itemacross different chatbots. For example, the content item can be selectedfor insertion in a dialog data structure identified by a first chatbot,such as a recipe chatbot. Subsequent to provision of the content itemwith the first dialog data structure of the first chatbot, a secondchatbot can be invoked via the computing device 104. The second chatbotcan be invoked based on a second digital file received via the computingdevice 104. The second chatbot can identify a second dialog datastructure responsive to the second digital file. For example, the seconddigital file can correspond to a request for movie times, the secondchatbot can be a movie chatbot, and the second dialog data structure canbe a list of one or more movie times, such as “Movie A is playing atLocal Theater today at 6 PM, 7:30 PM and 9:00 PM.” Since the identifierof the second chatbot and second dialog data structure may be differentfrom the first chatbot and first dialog data structure, the dataprocessing system 102 may not generate the same index value for thesecond chatbot as compared to the index value generated for the firstchatbot. However, the data processing system 102 can utilize differentidentifiers or techniques to determine to reuse the content item. Forexample, the data processing system 102 can determine that the movietheater identified by the second chatbot in the second dialog datastructure provides the good or service corresponding to the content itemselected for provision with the first dialog data structure. The dataprocessing system 102 can then determine that the identifier of aproduct associated with the content item, or keyword associated with thecontent item, matches or corresponds to metadata associated with thelocal movie theater identified by the second chatbot. Thus, the dataprocessing system 102 can determine to reuse the content item with thesecond dialog data structure, as follows: “Movie A is playing at LocalTheater today at 6 PM, 7:30 PM and 9:00 PM, which sells Brand A cola.”

The data processing system 102 may automatically insert the placeholderin the second dialog data structure, or the second dialog data structuremay be configured with the placeholder. For example, the second dialogdata structure can include a second placeholder as follows: “Movie A isplaying at Local Theater today at 6 PM, 7:30 PM and 9:00 PM. Get therein time to grab <second placeholder> before the show.” In this case, thesecond placeholder can be populated with brand name products, such assoda, candy, popcorn, ice cream, pizza, or other product. The secondplaceholder can be associated with content selection criteria ormetadata that indicates that a content selection process can usekeywords soda, candy, popcorn, ice cream, pizza to identify a contentitem. The data processing system 102 can perform a second contentselection process to identify a content item for the second placeholder,or determine to reuse a previously selected content item in order toreduce computational resource utilization. In some cases, the dataprocessing system 102 can determine to reuse the content item to reduceresource consumption based on a current load on the data processingsystem 102, historical load patterns, or bandwidth availability of thecomputing device 104. For example, if the time of day corresponds to apeak load for data processing system 102, then data processing system102 can determine to reuse the first content item instead of executing anew content selection process.

The data processing system 102 can identify the content item to reusebased on comparing the second placeholder field with the firstplaceholder field to determine a similarity. For example, the firstplaceholder field can have keyword “cola” and the second placeholderfield can also have keyword “cola.” In some cases, the first and secondplaceholder field can be the same placeholder field, such as aplaceholder field for a brand of cola, and have the same identifier. Insome cases, the identifier of the placeholder field can refer to one ormore keywords or content selection criteria associated with theplaceholder field. The index value generated for reuse of content itemsacross multiple chatbots can be generated using a hash of an identifierof the first placeholder field and the identifier of the computingdevice, where the identifier of the first placeholder field can includea keyword or topic of the placeholder field. The content item can beassociated with multiple index values if there are multiple keywords forthe placeholder field in which the content item is populated. The reuseof the content item can cross-over to multiple chatbots to providefurther optimizations or reductions in computation or energy resourceutilization.

The second dialog data structure identified by the second chatbot maynot include a second placeholder field, in which case the dataprocessing system 102 can, via placeholder generation component 116,insert the second placeholder field at a position in the second dialogdata structure, and populate the second placeholder field with thereused content item. For example, the data processing system 102 candetermine that metadata or keywords associated with the second dialogdata structure correspond to keywords associated with the content item,and determine, based on this relevancy or match, to insert a secondplaceholder and populate the second placeholder with the content item.In some cases, the data processing system 102 can determine to insertthe placeholder based on determining that a previously selected contentitem is relevant or corresponds to the second dialog data structure(e.g., based on one or more keyword matches, such as a broad, phrase orexact match).

In some cases, the second chatbot can identify the second dialog datastructure based on the previously selected content item. For example,the data processing system 102 can select a second dialog data structurehaving a grammar and syntax that allows for the insertion of a secondplaceholder field having the content item. The candidate second dialogdata structures can include placeholder fields at different positions,and the data processing system 102 can select a second dialog datastructure based on a parameter associated with the content item providedwith the first dialog data structure identified via the first chatbot.For example, the content item may indicate a position requirement in adialog data structure such as: beginning portion, within first threewords of sentence, within last three words of sentence, within middle 3words, adjacent to and preceding a noun, or not adjust to or proceedinga proper noun, adjective or verb. The data processing system 102, orsecond chatbot, can accordingly select a second dialog data structurethat satisfies criteria of the content item in order to reuse thecontent item. For example, the second chatbot can identify multiplestructures or configurations for the second dialog data structure, ormultiple candidate second dialog data structures, and the dataprocessing system 102 can select the second dialog data structure basedon the content item.

The first and second chatbots can have the same or different acousticfingerprint or voice. Since the content item can be configured for aparametrically driven text to speech technique, or otherwise convertedto voice, the first and second chatbot can assign native voiceparameters to the content item for provision with the respective dialogdata structures. Thus, the content item can be presented in a firstvoice to match the voice of the first dialog data structure identifiedby the first chatbot, and later played in a different voice that matchesthe voice of the second dialog data structure identified by the secondchatbot. The content item can appear to be in the native voice of thecorresponding chatbot or dialog data structure.

The data processing system 102 can reuse the content item with multiplechatbots in a same session, across multiple sessions, or after resuminga session break. For example, the data processing system 102 canidentify the session break based on temporal or location thresholds, anddetermine to resume the session based on a subsequently received digitalfile (e.g., matching keywords, topic, or category), and then reuse thecontent item. In some cases, the second chatbot can query the firstchatbot to identify the content item used by the first chatbot, whichcan be stored in chatbot data data structure 132 or on third-partychatbot platform 146.

The data processing system 102 can include a validation component 122designed, constructed and operational to validate a third-party chatbotplatform using a validation technique. Validation can refer todetermining whether the content item selected by the data processingsystem 102 for inclusion with the dialog data structure was indeed thecontent item that the third-party chatbot platform 146 provided to thechatbot. The data processing system 102 can perform validation todetermine whether the third-party chatbot platform 146 accurately andreliably forwarded the selected content item to the chatbot. Forexample, the third-party chatbot platform 146 may contain errors, bugs,faults, or other technical problems that result in an incorrect contentitem being forwarded to the chatbot. By validating the third-partychatbot platform 146, the data processing system 102 can determine todisable the provision of content items or otherwise alert thethird-party chatbot platform 146 or chatbot provider device 108 orcontent provider device 106.

For example, the data processing system 102 can provide a content itemto a third-party chatbot server with instructions to forward the contentitem to the chatbot for insertion in a dialog data structure. The dataprocessing system 102 (e.g., via the validation component 122) can thenping the chatbot for the content item, and compare the response from thechatbot with the content item that was initially provided to thethird-party chatbot platform to determine whether they match, or if thethird-party chatbot platform modified the content item.

Subsequent to transmission of the content item for provision with thedialog data structure, the validation component 122 can ping the chatbotfor the content item. The data processing system 102 can ping thechatbot or computing device 104 executing the chatbot, or third-partychatbot platform 146. The data processing system 102 can ping thechatbot provider device 108, which may store information related to thecontent item. The data processing system 102 can ping the contentprovider device 106 for network activity associated with presentation ofthe content item. The data processing system 102 can ping the chatbotbased on a time interval. The time interval can include a time intervalsubsequent to provision of the content item, or be based on a timingfunction. The timing function can produce a random time interval withina range, such as 5 minutes after transmission of the content item, 10minutes after transmission of the content item, 30 minutes aftertransmission of the content item, 5 seconds after transmission of thecontent item, between 10 seconds and 5 minutes of transmission of thecontent item, between 1 minute and 30 minutes of transmission of thecontent item, or other range. By using a timing function that can berandom or pseudo random (e.g., determined using a random numbergenerator), the data processing system 102 can mitigate fraud ormalicious acts of the third-party chatbot platform.

The data processing system 102 can receive, from the chatbot, anindication of the content item responsive to the ping. The indicationcan include an identifier of the content item. The indication caninclude information about how the content item was presented, such as anidentifier of the chatbot, placeholder field, keywords, timestamp, orcomputing device 104. The data processing system 102 can compare thereceived indication with information associated with the content itemprovided by the data processing system 102. For example, the dataprocessing system 102 can compare a first timestamp at which the dataprocessing system 102 transmitted the content item to the third-partychatbot platform with a second timestamp at which the chatbot insertedor presented the content item. The data processing system can set avalidation parameter based on a difference between the first timestampand the second timestamp. For example, if the time difference is greaterthan 30 seconds, 1 minute, 2 minutes, 5 minutes, 10 minutes or more,then the data processing system 102 can determine that the delay is anindication of a technical problem (e.g., high network latency, highprocessor latency, or issues with the platform code or chatbot program).The data processing system 102 can set a validation parameter based on acomparison of the identifier of the content item provided to thethird-party chatbot platform 146, and the indication of the content itemreceived from the chatbot.

The data processing system 102 can set a validation parameter based onthe comparison of the received indication of the content item with theidentifier of the content item selected by the data processing system102. If the data processing system 102 determines, based on thecomparison, that the content items do not match (e.g., chatbot did notreceive the same content item selected by the data processing system 102for the chatbot), the data processing system 102 can set the validationparameter to indicate invalidity (e.g., 0, N, or other alphanumericvalue or symbol that indicates invalidity, fraud or malicious activity).If the data processing system 102 determines that the content itemsmatch, then the data processing system 102 can set the validationparameter to indicate that the third-party chatbot platform 146 isvalid.

The data processing system 102 can set the validation parameter based ona statistical analysis of multiple pings to one or more chatbotsprovided by the third-party chatbot platform 146. For example, ifgreater than a threshold percentage (e.g., 0.1%, 0.5%, 1%, 2%, 5%, 10%,15%, 20% or more) of pings results in determining a mismatch between thecontent item provided by the data processing system 102 and the contentitem forwarded to the third-party chatbot platform 146, then the dataprocessing system 102 can flag or set the validation parameter asinvalid for the third-party chatbot platform 146.

In some cases, the data processing system 102 can disable the provisionof content items to chatbots provided by the third-party chatbotplatform 146 responsive to the validation parameter being invalid. Forexample, the data processing system 102 can ignore or disregard requestsfor content from the third-party chatbot platform 146, thereby reducingcomputational resource utilization by not wasting processor and memoryutilization by performing a content selection process for a third-partychatbot platform 146 that may be a malicious actor or otherwise havetechnical problems that result in the presentation of an incorrectcontent item or delayed presentation of the content item.

The data processing system 102 can receive indications of interactionswith the content item. The interaction with the content item can occurvia an interface or component of the computing device 104. The dataprocessing system 102 can record, store or otherwise monitor and trackthe interactions with the content item, and information about orassociated with the interaction. The data processing system 102 canfurther record, store or otherwise tag the interaction as being validbased on application of the validation technique to the third-partychatbot platform 146. For example, if the data processing system 102determines that the content item selected and provided by the dataprocessing system to the third-party chatbot platform 146 matches thecontent item presented by the chatbot, the data processing system 102can record a subsequent interaction with the content item via thecomputing device 104 as being a valid interaction. If, however, the dataprocessing system 102 determines that the content items do not match,then the data processing system 102 can mark or flag the interaction asinvalid. The data processing system 102 can generate and transmit analert to a content provider device 106 responsive to detecting theinvalid interaction. The content provider device 106 can correspond tothe provider of the content item selected by the data processing system102, which can be different form the provider of the content item thatwas actually provided to and presented by the chatbot.

In some cases, the data processing system 102 can encrypt the contentitem prior to transmitting the content item to the third-party chatbotplatform 146. The chatbot can include the key to decrypt the contentitem prior to presenting the content item. By encrypting the contentitem, the data processing system 102 can securely transmit the contentitem without the content item being manipulated by an intermediarydevice.

The encryption technique can include a hash function. The encryptiontechnique can include hashing the content item with salts. Salts canrefer to data (e.g., random data or predetermined data that is not knownto the third-party chatbot platform 146) that can be used as anadditional input to a function that hashes the content item. The dataprocessing system 102 can store the content item encrypted with a hashfunction and salts. The data processing system 102 can provide theencrypted content item to the third-party chatbot platform 146 forforwarding to the chatbot. The chatbot can be configured with adecryption technique to decrypt the content item encrypted with the hashfunction and salts such that the content item can be recovered andpresented. Since the third-party chatbot platform 146 may not haveaccess to the encryption function, hash function, or salts, a fraudulentor incorrect content item provided by the third-party chatbot platform146 to the chatbot would not be properly decrypted and presented by thechatbot. Thus, the encryption technique can be used to prevent thefraudulent or inaccurate presentation of content items by the chatbot.

FIG. 2 is an illustration of an operation of a system 200 to modifycomputer program output via a network. At ACT 202, the client computingdevice 104 can receive voice input (or other non-text input such asimage or video input). The voice input can be spoken by a user of thecomputing device 104. The computing device 104 can convert the voiceinput from an analog format (e.g., as detected by a transducer ormicrophone) to a digital format. The computing device 104 can transmitthe digital format of the voice input in a digital file at ACT 204 tothe data processing system 102.

In some cases, the input 202 can include image or video input inaddition to, or instead of, voice input. The computing device 104 canuse one or more imaging techniques (e.g., computer vision, machinelearning, image recognition, or image interpretation) to process oranalyze the image, and convert the image to the digital file. In somecases, the computing device 104 can convert the image or video input toa digital file without performing an imaging technique to furtherprocess, analyze or interpret the image. Instead, the computing device104 can forward or transmit the digital file corresponding to the inputimage or video to the data processing system for further imageprocessing. The images and video input can be sensed or detected by thecomputing device 104 and converted to the digital file, or the computingdevice 104 can retrieve the images or video from a remote source, suchas a data repository, external storage device, third-party data storageunit via a network, or other computing device. The image and video inputcan be processed or parsed to obtain the same type of information thatis obtained when the voice input 202 is processed or parsed.

The data processing system 102 can process the digital file and invoke achatbot at ACT 206. The data processing system 102 can invoke thechatbot based on a trigger keyword or semantic processing of the digitalfile. At ACT 208, a recipe chatbot, for example, invoked by the dataprocessing system 102 can identify a recipe requested by the voice inputat ACT 202. For example, the identified recipe can be for chicken wings.The recipe can be stored in a dialog data structure. The dialog datastructure can include text. For example, the text can be the ingredientsin the identified recipe for the chatbot. The text can also includeinstructions on how to prepare the ingredients to make the chickenwings, or other information that can facilitate preparation of thechicken wings.

The recipe chatbot can further identify a placeholder field in thedialog data structure at ACT 208. For example, the dialog data structurewith the placeholder field can be “Ingredients: 1 cup brown sugar, 1 can<placeholder> cola, 2 medium onions, 2 cloves garlic” 210. Responsive toidentifying the <placeholder> field, the data processing system 102 canreceive a request for content at ACT 212. The chatbot can generate therequest, the third-party chatbot platform can generate the request, thecomputing device can generate the request, or a component of the dataprocessing system can generate the request. The data processing system102 can receive the request and select a content item. The dataprocessing system 102 can provide the selected content item at ACT 214.The data processing system 102 can provide the content item to thechatbot, to the client computing device 104, the third-party chatbotplatform or other entity. The data processing system 102 can, in somecases, integrate or embed the content item with the dialog datastructure. For example, if the data processing system 102 receives orhas access to the dialog data structure 210, the data processing system102 can embed the content item in the dialog data structure 210, andprovide the modified dialog data structure that includes the contentitem to the client computing device 104. The client computing device 104can output the modified dialog data structure at ACT 216 as follows:“Ingredients: 1 cup brown sugar, 1 can Brand_Name cola, 2 medium onions,2 cloves garlic” 218.

The data processing system 102 can determine that ACTS 202 through 218correspond to Session 1. The data processing system can then determine asession break. For example, the data processing system 102 can receivesensor input 220 that indicates a break, such as location information,timer information, physical activity information, voice input, or thelack of sensory input indicating an idle state of use. At ACT 222, thedata processing system 102 can detect the session break based on thesensor input 220 (or lack thereof), generate an index value for theselected content item to potentially reuse the content item in a latersession or upon resumption of the session, and store the content item inassociation with the index value in memory 224.

FIG. 3 is an illustration of an operation of a system 300 to balancedata requests for modification of computer program output based on asession. At ACT 302, the computing device 104 receives voice input (orother non-text input such as image or video). At ACT 304, the clientcomputing device can transmit a digital file corresponding to thereceived voice input to the data processing system 120. The dataprocessing system 102 can process the digital file to determine that thevoice input corresponds to a previous Session 1, as illustrated in FIG.2. For example, the voice input can correspond to how to prepare theingredients presented in 210 in order to prepare the dish.

In some cases, the input 302 can include image or video input inaddition to, or instead of, voice input. The computing device 104 canuse one or more imaging techniques (e.g., computer vision, machinelearning, image recognition, or image interpretation) to process oranalyze the image, and convert the image to the digital file. In somecases, the computing device 104 can convert the image or video input toa digital file without performing an imaging technique to furtherprocess, analyze or interpret the image. Instead, the computing device104 can forward or transmit the digital file corresponding to the inputimage or video to the data processing system for further imageprocessing. The data processing system 102 can process the digital filecorresponding to the image or video input to determine that the input302 corresponds to the previous Session 1. The image and video input canbe processed or parsed to obtain the same type of information that isobtained when the voice input 302 is processed or parsed.

At ACT 306, the data processing system 102 can resume the session withthe same recipe chatbot, which can identify the same recipe 308 andinstructions to prepare the recipe as follows: “Combine in pan: 1 cupbrown sugar, 1 can <placeholder> cola, 2 medium onions, 2 cloves garlic”310. The preparation instructions can be in the form of a second dialogdata structure. The second dialog data structure can include a secondplaceholder field. The chatbot, or other related device or system, cantransmit a request for content to the data processing system 102 at ACT312. The data processing system 102, responsive to the request, canselect the same content item provided at ACT 214 previously duringSession 1. The data processing system 102 can select the same contentbased on identifiers and a generated index value that is associated withthe content item in memory. At ACT 314, the data processing system 102can provide the same content item from session 1 to the client computingdevice 104. At ACT 316, the client computing device can output thedialog data structure modified with the content item as follows:“Combine in pan: 1 cup brown sugar, 1 can Brand_Name cola, 2 mediumonions, 2 cloves garlic” 318.

At ACT 320, the data processing system can receive sensor input (e.g.,from computing device 104), and detect a session break at ACT 322. Thedata processing system 102 can store, in memory, the index valueassociated with the second presentation of the content item in memory324. The data processing system 102 can store the second index value inaddition to the previously generated index value. The index values maybe the same, or additional index values can be generated for associatedwith the content item. For example, the second dialog data structure maybe associated with additional identifiers or keyword as compared to thefirst dialog data structure.

FIG. 4 is an illustration of an operation of a system 400 to balancedata requests for modification of computer program output based on asession. At ACT 402, the client computing device 104 can receive voiceinput (or other input such as image, video, or other non-text input). AtACT 404, the client computing device transmits a digital filecorresponding to the voice input to the data processing system 102. Thedata processing system 102 can process the digital file and select achatbot. The data processing system 102 can further determine to resumea previous Session 1. For example, even though the data processingsystem 102 (or third-party chatbot provider platform 146) invokes a newchatbot, the data processing system 102 can determine to resume previousSession 1 based on other attributes associated with the digital file(e.g., same computing device 104, temporal information, locationinformation, semantic analysis, opportunities for insertion of the samecontent item, or historical network activity indicative of interactionwith the content item presented in Session 1).

In some cases, the input 402 can include image or video input inaddition to, or instead of, voice input. The computing device 104 canuse one or more imaging techniques (e.g., computer vision, machinelearning, image recognition, or image interpretation) to process oranalyze the image, and convert the image to the digital file. In somecases, the computing device 104 can convert the image or video input toa digital file without performing an imaging technique to furtherprocess, analyze or interpret the image. Instead, the computing device104 can forward or transmit the digital file corresponding to the inputimage or video to the data processing system for further imageprocessing. The data processing system 102 can process the digital filecorresponding to the image or video input to determine that the input302 corresponds to the previous Session 1. The images and videos can beprocessed or parsed to obtain the same type of information that isobtained when the voice input 402 is processed or parsed.

At ACT 406, the data processing system 102 resumes Session 1 with asecond chatbot. The second chatbot can be, for example, a movie chatbot.The movie chatbot can identify, based on the request or query in thedigital file 404, a dialog data structure for theater and show timeinformation 408. However, in this example, the theater and show timedialog data structure may not include a placeholder as follows: “ActionMovie Playing at Local Theater at 8 PM” 410.

At ACT 412, the chatbot (or other entity) can provide the identifieddialog data structure 410 to the data processing system 102. In somecases, the data processing system 102 can intercept the dialog datastructure 410. For example, the data processing system 102 can beintermediary to the computing device and third-party chatbot platform146. The data processing system 102 can parse or otherwise process thesecond dialog data structure and determine (e.g., via a placeholdergeneration component) to insert a placeholder field or directly insert acontent item into the dialog data structure. The data processing system102 can provide the modified dialog data structure with the same contentitem selected form Session 1 to the client computing device at ACT 414.At ACT 416, the client computing device 104 can output the dialog datastructure with the content item as follows: “Action Movie Playing atLocal Theater at 8 PM, which also serves Brand_Name Cola” 418. In thisexample, since the data processing system 102 is inserting the contentitem where there is no placeholder field, the data processing system canadd a phrase to integrate the content item “Brand_Name” into the dialogdata structure. The data processing system 102 can modify the grammar orsyntax of the dialog data structure to integrate the content item. Thedata processing system 102 can be pre-configured with patterns thatfacilitate identifying a grammar or syntax of the dialog data structure,and identifying a template to use to modify the grammar or syntax of thedialog data structure.

FIG. 5 is an illustration of an operation of a system 500 to validatemodification of computer program output via a network. At ACT 502, thedata processing system 102 can receive a request for content from athird-party chatbot platform 146. At ACT 504, the data processing system102 can perform a content selection process and provide the content itemto the third-party chatbot platform 146. At ACT 506, the third-partychatbot platform 146 can provide the content item to a chatbot 508. Thechatbot 508 can be a computer program executing on the third-partychatbot platform 146 or a client computing device 104. At ACT 510, thechatbot 508 can provide, to the client computing device 104, a dialogdata structure modified with the content item. To determine whether thecontent item provided at ACT 510 to the computing device 104 is the samecontent item provided by the data processing system 102 to thethird-party chatbot platform 146 at ACT 504, the data processing system102 can transmit a validation ping at ACT 512 to the chatbot 508. Thevalidation ping 512 can request an indication of the content itemprovided at ACT 510 to the computing device 104. At ACT 514, the chatbot508 can response to the validation ping with an indication of thecontent item, such as a content item identifier, keywords of the contentitem, a timestamp of the content item or other information indicative ofthe presentation of the content item.

The data processing system 102 can compare the indication of the contentitem received at ACT 514 with the content item provided at ACT 5045 toset a validation parameter 516. For example, if the content items match,then the data processing system 102 can validate the third-party chatbotplatform 146. If the content items do not match, the data processingsystem 102 can invalidate the third-party chatbot platform or generatean alert or notification. The data processing system 102 can furthervalidate or invalidate the platform 146 based on a delay between whenthe content item was provided to the platform 146 at ACT 504 and whenthe content item was provided to the chatbot at ACT 506, or at ACT 510to the computing device 104.

The systems or operational flows 200, 300, 400, and 500 depicted inFIGS. 2-5 can include one or more component or functionality of system100 depicted in FIG. 1. For example, the systems or operational flows200, 300, 400, and 500 can include or be performed by a data processingsystem 102, client computing device 104, third-party chatbot providerdevice 146, or content provider device 106.

FIG. 6 is an illustration of a method of modifying computer programoutput via a computer network. The method 600 can performed by one ormore component or system depicted in FIGS. 1-5, including for example,system 100, data processing system 102, computing device 104,third-party chatbot platform provider 146, chatbot provider device 108,or content provider computing device 106. At ACT 602, the dataprocessing system can receive a digital file. The data processing systemcan receive the digital file from a computing device or a third-partychatbot platform. The digital file can correspond to voice inputdetected by a microphone of the computing device. The digital file caninclude a digitized representation of the analog voice input.

At ACT 604, the data processing system or third-party chatbot platformcan select a computer program comprising a chatbot and invoke thechatbot. In some cases, the data processing system 102 can select andinvoke the chatbot.; a third-party chatbot platform can select andinvoke the chatbot; or the computing device can select and invoke thechatbot. The chatbot can be selected and invoked prior to transmittingthe digital file to the data processing system.

At ACT 606, a placeholder field in the dialog data structure can beidentified. At ACT 608, and responsive to identifying the placeholderfield, a request for content can be generated and transmitted to thedata processing system. In some cases, the data processing system maynot receive the digital file corresponding to the acoustic signal. Forexample, the data processing system can receive a request for contentand information to facilitate content selection, instead of the digitalfile. The data processing system can receive the request for contentfrom the third-party chatbot platform, chatbot, or computing device.

At ACT 610, the data processing system can select the content itemresponsive to the request, and provide the content item. The dataprocessing system can provide the selected content item to thethird-party chatbot platform, computing device, or other entity thatrequested the content item. The data processing system can insert thecontent item into the dialog data structure, and provide the modifieddialog data structure for presentation via the computing device.

The data processing system can automatically generate placeholder fieldsfor insertion into dialog data structure. The data processing system canuse a template, pattern, semantic analysis, policy or rule to determinewhether to insert a placeholder into a dialog data structure, and whereto insert the placeholder field in the dialog data structure. In somecases, the chatbot developer can request the data processing system todetermine where in the dialog data structure to insert the placeholderfield. A policy or rule based on semantic analysis can include the dataprocessing system identifying a noun in the dialog data structure,generating a keyword based on the noun, and then using the keyword toperform a content selection process to determine if there are contentitems provided by content providers that might match or otherwise berelevant to the noun in the dialog data structure. For example, the nouncan be “soda”. The data processing system can parse the noun “soda” togenerate one or more keywords, such as “soda”, “drink”, “soft drink”,“cola”, “pop”, or “soda pop”. The data processing system can use thekeyword to identify content items. The data processing system candetermine to insert the placeholder field. The placeholder field can beassociated with the keywords, metadata, positioning information, orother information associated with the dialog data structure. Theplaceholder can be associated with an identifier of the placeholderfield. In some cases, the data processing system can determine to insertthe placeholder field responsive to identifying at least one contentthat has a relevancy score greater than a threshold with respect to thenoun in the dialog data structure.

Thus, the data processing system can generate the second placeholderfield for the second dialog data structure and compare the secondplaceholder field with the first placeholder field of the first dialogdata structure to determine, based on the comparison, whether togenerate a second request for second content in the parameterizedformat. For example, the data processing system can compare theidentifiers of the placeholder fields, keywords of the placeholderfield, metadata, or position information (e.g., within first three wordsof dialog data structure, or last three words). The data processingsystem can determine, based on comparison of the placeholder fields,that there is a similarity between the placeholder field (e.g., similaror same keywords). For example, both placeholder fields can be for abrand of soda, in which case the data processing system can reuse thecontent item from the first placeholder field. The data processingsystem can determine, based on the comparison, not to request a secondcontent item for the second placeholder field of the second dialog datastructure because the data processing system can reuse the content itemselected for the first placeholder field for insertion into the secondplaceholder field of the second dialog data structure.

If, however, the data processing system determines based on thecomparison that the placeholder fields are not similar or different(e.g., different keywords or keywords do not match), the data processingsystem can determine to select a new, second content item. For example,the first placeholder field (or first dialog data structure) can beassociated with keywords for soda, while the second placeholder field(or second dialog data structure) can be associated with keywords forluxury cars.

FIG. 7 is an illustration of a method of balancing data requests tomodify computer program output via a computer network. The method 700can performed by one or more component or system depicted in FIGS. 1-5,including for example, system 100, data processing system 102, computingdevice 104, third-party chatbot platform provider 146, chatbot providerdevice 108, or content provider computing device 106. At ACT 702, thedata processing system can receive a digital file. The data processingsystem can receive the digital file from a computing device or athird-party chatbot platform. The digital file can correspond to voiceinput detected by a microphone of the computing device. The digital filecan include a digitized representation of the analog voice input. Thedigital file can be pre-processed to include keywords or tokensassociated with the voice input. In some cases, the data processingsystem may not receive the digital file and, instead, receive therequest for content with information about the digital file that canfacilitate content selection.

At ACT 704, the data processing system or third-party chatbot platformcan select a computer program comprising a chatbot and invoke thechatbot. In some cases, the data processing system 102 can select andinvoke the chatbot.; a third-party chatbot platform can select andinvoke the chatbot; or the computing device can select and invoke thechatbot. The chatbot can be selected and invoked prior to transmittingthe digital file to the data processing system.

At ACT 706, a placeholder field in the dialog data structure can beidentified. At ACT 708, the data processing system can select thecontent item and provide the content item. The data processing systemcan provide the selected content item to the third-party chatbotplatform, computing device, or other entity that requested the contentitem. The data processing system can insert the content item into thedialog data structure, and provide the modified dialog data structurefor presentation via the computing device.

At ACT 710, the data processing system can facilitate reducing theutilization of computing resources by storing the content item inmemory. The data processing system can associate the content item withan index value based on information associated with the presentation ofthe content item. For example, the index value can be generated based onone or more identifiers associated with the content item, computingdevice, chatbot, dialog data structure, keyword, topic, or location. Theindex value can be generated based on identifiers that are relevant tothe session and can be used to determine whether to resume the session,identify which session to resume, or start a new session.

At ACT 712, the data processing system can receive a second digital fileor a second request for content with information to facilitate contentselection. At ACT 714, the data processing system can select or invokethe same chatbot that was invoked at ACT 704. For cases in which anotherdevice or entity selects and invokes the chatbot, the data processingsystem at ACT 714 can receive an indication of the chatbot that isactive.

At ACT 716, the data processing system can determine to provide the samecontent item provided at ACT 708. The data processing system candetermine, for example, that the same content item is relevant to thesecond dialog data structure.

The data processing system can determine to reuse content items based onthe voice input, or determine not to reuse content items based on thecomparison of voice input with previous voice input. For example, if thelater received voice input is different from the previously receivedvoice input with respect to keywords, content, context, or otherparameters, the data processing system can determine not to reuse thecontent item. In some cases, the data processing system can determine tonot reuse content items from a different session. Two session can bedifferent if, for example, they are separated in time by greater than athreshold, correspond to different end users, have different voice inputwith different acoustic fingerprints, or different geographic locations.

For example, the data processing system can receive a third orsubsequent digital file corresponding to a fifth or subsequent acousticsignal carrying third or subsequent voice content detected by themicrophone on the computing device. The data processing system canselect, responsive to the third voice content of the third digital file,the computer program comprising the chatbot. The data processing systemcan identify, via the chatbot based on the third voice content of thethird digital file, a third or subsequent dialog data structurecomprising a third placeholder field. The data processing system cangenerate a second or new index value based on a combination of the firstidentifier, the third identifier, and a fifth identifier of the thirddialog data structure. The data processing system can determine, basedon a comparison of the index value with the second index value, not toreuse the content item. The data processing system can select,responsive to identification of the third placeholder and based on acombination of the first identifier of the chatbot, the third identifierof the computing device, and the fifth identifier of the third dialogdata structure, a second content item to provision to the computingdevice to cause the computing device to perform the parametricallydriven text to speech technique to generate a sixth or subsequentacoustic signal corresponding to the third dialog data structuremodified with the second content item.

FIG. 8 is an illustration of a method of balancing data requests tomodify computer program output via a computer network. The method 800can performed by one or more component or system depicted in FIGS. 1-5,including for example, system 100, data processing system 102, computingdevice 104, third-party chatbot platform provider 146, chatbot providerdevice 108, or content provider computing device 106. At ACT 802, thedata processing system can receive a digital file. The data processingsystem can receive the digital file from a computing device or athird-party chatbot platform. The digital file can correspond to voiceinput detected by a microphone of the computing device. The digital filecan include a digitized representation of the analog voice input. Thedigital file can be pre-processed to include keywords or tokensassociated with the voice input. In some cases, the data processingsystem may not receive the digital file and, instead, receive therequest for content with information about the digital file that canfacilitate content selection.

At ACT 804, the data processing system or third-party chatbot platformcan select a computer program comprising a chatbot and invoke thechatbot. In some cases, the data processing system 102 can select andinvoke the chatbot.; a third-party chatbot platform can select andinvoke the chatbot; or the computing device can select and invoke thechatbot. The chatbot can be selected and invoked prior to transmittingthe digital file to the data processing system.

At ACT 806, a placeholder field in the dialog data structure can beidentified. At ACT 808, the data processing system can select thecontent item and provide the content item. The data processing systemcan provide the selected content item to the third-party chatbotplatform, computing device, or other entity that requested the contentitem. The data processing system can insert the content item into thedialog data structure, and provide the modified dialog data structurefor presentation via the computing device.

At ACT 810, the data processing system can facilitate reducing theutilization of computing resources by storing the content item inmemory. The data processing system can associate the content item withan index value based on information associated with the presentation ofthe content item. For example, the index value can be generated based onone or more identifiers associated with the content item, computingdevice, chatbot, dialog data structure, keyword, topic, or location. Theindex value can be generated based on identifiers that are relevant tothe session and can be used to determine whether to resume the session,identify which session to resume, or start a new session.

At ACT 812, the data processing system can receive a second digital fileor a second request for content with information to facilitate contentselection. At ACT 814, the data processing system can select a secondcomputer program comprising a second chatbot that is different from thepreviously invoked chatbot at ACT 804. The data processing system canreceive an indication of the second chatbot, which may be selected by athird-party. At ACT 816, the data processing system can determine thatthe previously selected content item at ACT 808 is relevant to a seconddialog data structure being provided by the second chatbot, anddetermine to reuse the same content item in order to reduce resourceutilization.

FIG. 9 is an illustration of a method of validating computer programoutput via a computer network. The method 900 can performed by one ormore component or system depicted in FIGS. 1-5, including for example,system 100, data processing system 102, computing device 104,third-party chatbot platform provider 146, chatbot provider device 108,or content provider computing device 106. At ACT 902, the dataprocessing system can establish a communication channel with a server ofa third-party chatbot platform. The communication channel can be secureby, for example, utilization an encryption technique. The dataprocessing system can use a handshaking protocol to establish thecommunication channel.

At ACT 904, the data processing system can receive a request for contentfrom the third-party server. The request for content can be triggered bythe chatbot. The request for content can be responsive to the chatbotgenerating a query. At ACT 906, the data processing system can select acontent item and provide the content item to the third-party server ofthe third-party chatbot platform. The third-party server can beinstructed to forward the content item to the chatbot for presentationto a user of a computing device.

At ACT 908, the data processing system can receive an indication of thecontent item from the chatbot. For example, the data processing systemcan ping the chatbot for the indication of the content item. At ACT 910,the data processing system can set a validation parameter based on acomparison of the indication of the content item and the selectedcontent item provided by the data processing system to the third-partyserver of the third-party chatbot platform. The validation parameter canindicate whether the content items match or whether the content item wastimely provided to the chatbot.

FIG. 10 is a block diagram of an example computer system 1000. Thecomputer system or computing device 1000 can include or be used toimplement the system 100, or its components such as the data processingsystem 102. The data processing system 102 can include an intelligentpersonal assistant or voice-based digital assistant. The computingsystem 1000 includes a bus 1005 or other communication component forcommunicating information and a processor 1010 or processing circuitcoupled to the bus 1005 for processing information. The computing system1000 can also include one or more processors 1010 or processing circuitscoupled to the bus for processing information. The computing system 1000also includes main memory 1015, such as a random access memory (RAM) orother dynamic storage device, coupled to the bus 1005 for storinginformation, and instructions to be executed by the processor 1010. Themain memory 1015 can be or include the data repository 145. The mainmemory 1015 can also be used for storing position information, temporaryvariables, or other intermediate information during execution ofinstructions by the processor 1010. The computing system 1000 mayfurther include a read-only memory (ROM) 1020 or other static storagedevice coupled to the bus 1005 for storing static information andinstructions for the processor 1010. A storage device 1025, such as asolid-state device, magnetic disk or optical disk, can be coupled to thebus 1005 to persistently store information and instructions. The storagedevice 1025 can include or be part of the data repository 145.

The computing system 1000 may be coupled via the bus 1005 to a display1035, such as a liquid crystal display, or active matrix display, fordisplaying information to a user. An input device 1030, such as akeyboard including alphanumeric and other keys, may be coupled to thebus 1005 for communicating information and command selections to theprocessor 1010. The input device 1030 can include a touch screen display1035. The input device 1030 can also include a cursor control, such as amouse, a trackball, or cursor direction keys, for communicatingdirection information and command selections to the processor 1010 andfor controlling cursor movement on the display 1035. The display 1035can be part of the data processing system 102, the client computingdevice 150 or other component of FIG. 1, for example.

The processes, systems and methods described herein can be implementedby the computing system 1000 in response to the processor 1010 executingan arrangement of instructions contained in main memory 1015. Suchinstructions can be read into main memory 1015 from anothercomputer-readable medium, such as the storage device 1025. Execution ofthe arrangement of instructions contained in main memory 1015 causes thecomputing system 1000 to perform the illustrative processes describedherein. One or more processors in a multi-processing arrangement mayalso be employed to execute the instructions contained in main memory1015. Hard-wired circuitry can be used in place of or in combinationwith software instructions together with the systems and methodsdescribed herein. Systems and methods described herein are not limitedto any specific combination of hardware circuitry and software.

Although an example computing system has been described in FIG. 10, thesubject matter including the operations described in this specificationcan be implemented in other types of digital electronic circuitry, or incomputer software, firmware, or hardware, including the structuresdisclosed in this specification and their structural equivalents, or incombinations of one or more of them.

For situations in which the systems discussed herein collect personalinformation about users, or may make use of personal information, theusers may be provided with an opportunity to control whether programs orfeatures that may collect personal information (e.g., information abouta user's social network, social actions or activities, a user'spreferences, or a user's location), or to control whether or how toreceive content from a content server or other data processing systemthat may be more relevant to the user. In addition, certain data may beanonymized in one or more ways before it is stored or used, so thatpersonally identifiable information is removed when generatingparameters. For example, a user's identity may be anonymized so that nopersonally identifiable information can be determined for the user, or auser's geographic location may be generalized where location informationis obtained (such as to a city, postal code, or state level), so that aparticular location of a user cannot be determined. Thus, the user mayhave control over how information is collected about him or her and usedby the content server.

The subject matter and the operations described in this specificationcan be implemented in digital electronic circuitry, or in computersoftware, firmware, or hardware, including the structures disclosed inthis specification and their structural equivalents, or in combinationsof one or more of them. The subject matter described in thisspecification can be implemented as one or more computer programs, e.g.,one or more circuits of computer program instructions, encoded on one ormore computer storage media for execution by, or to control theoperation of, data processing apparatuses. Alternatively, or inaddition, the program instructions can be encoded on an artificiallygenerated propagated signal, e.g., a machine-generated electrical,optical, or electromagnetic signal that is generated to encodeinformation for transmission to suitable receiver apparatus forexecution by a data processing apparatus. A computer storage medium canbe, or be included in, a computer-readable storage device, acomputer-readable storage substrate, a random or serial access memoryarray or device, or a combination of one or more of them. While acomputer storage medium is not a propagated signal, a computer storagemedium can be a source or destination of computer program instructionsencoded in an artificially generated propagated signal. The computerstorage medium can also be, or be included in, one or more separatecomponents or media (e.g., multiple CDs, disks, or other storagedevices). The operations described in this specification can beimplemented as operations performed by a data processing apparatus ondata stored on one or more computer-readable storage devices or receivedfrom other sources.

The terms “data processing system” “computing device” “component” or“data processing apparatus” encompass various apparatuses, devices, andmachines for processing data, including by way of example a programmableprocessor, a computer, a system on a chip, or multiple ones, orcombinations of the foregoing. The apparatus can include special purposelogic circuitry, e.g., an FPGA (field programmable gate array) or anASIC (application specific integrated circuit). The apparatus can alsoinclude, in addition to hardware, code that creates an executionenvironment for the computer program in question, e.g., code thatconstitutes processor firmware, a protocol stack, a database managementsystem, an operating system, a cross-platform runtime environment, avirtual machine, or a combination of one or more of them. The apparatusand execution environment can realize various different computing modelinfrastructures, such as web services, distributed computing and gridcomputing infrastructures. For example, the interface 110, contentselector component 118, or NLP component 112 and other data processingsystem 102 components can include or share one or more data processingapparatuses, systems, computing devices, or processors.

A computer program (also known as a program, software, softwareapplication, app, script, or code) can be written in any form ofprogramming language, including compiled or interpreted languages,declarative or procedural languages, and can be deployed in any form,including as a stand-alone program or as a module, component,subroutine, object, or other unit suitable for use in a computingenvironment. A computer program can correspond to a file in a filesystem. A computer program can be stored in a portion of a file thatholds other programs or data (e.g., one or more scripts stored in amarkup language document), in a single file dedicated to the program inquestion, or in multiple coordinated files (e.g., files that store oneor more modules, sub programs, or portions of code). A computer programcan be deployed to be executed on one computer or on multiple computersthat are located at one site or distributed across multiple sites andinterconnected by a communication network.

The processes and logic flows described in this specification can beperformed by one or more programmable processors executing one or morecomputer programs (e.g., components of the data processing system 102)to perform actions by operating on input data and generating output. Theprocesses and logic flows can also be performed by, and apparatuses canalso be implemented as, special purpose logic circuitry, e.g., an FPGA(field programmable gate array) or an ASIC (application specificintegrated circuit). Devices suitable for storing computer programinstructions and data include all forms of non-volatile memory, mediaand memory devices, including by way of example semiconductor memorydevices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks,e.g., internal hard disks or removable disks; magneto optical disks; andCD ROM and DVD-ROM disks. The processor and the memory can besupplemented by, or incorporated in, special purpose logic circuitry.

The subject matter described herein can be implemented in a computingsystem that includes a back end component, e.g., as a data server, orthat includes a middleware component, e.g., an application server, orthat includes a front end component, e.g., a client computer having agraphical user interface or a web browser through which a user caninteract with an implementation of the subject matter described in thisspecification, or a combination of one or more such back end,middleware, or front end components. The components of the system can beinterconnected by any form or medium of digital data communication,e.g., a communication network. Examples of communication networksinclude a local area network (“LAN”) and a wide area network (“WAN”), aninter-network (e.g., the Internet), and peer-to-peer networks (e.g., adhoc peer-to-peer networks).

The computing system such as system 100 or system 1000 can includeclients and servers. A client and server are generally remote from eachother and typically interact through a communication network (e.g., thenetwork 165). The relationship of client and server arises by virtue ofcomputer programs running on the respective computers and having aclient-server relationship to each other. In some implementations, aserver transmits data (e.g., data packets representing a content item)to a client device (e.g., for purposes of displaying data to andreceiving user input from a user interacting with the client device).Data generated at the client device (e.g., a result of the userinteraction) can be received from the client device at the server (e.g.,received by the data processing system 102 from the computing device 150or the content provider computing device 155 or the chatbot providercomputing device 160).

While operations are depicted in the drawings in a particular order,such operations are not required to be performed in the particular ordershown or in sequential order, and all illustrated operations are notrequired to be performed. Actions described herein can be performed in adifferent order.

The separation of various system components does not require separationin all implementations, and the described program components can beincluded in a single hardware or software product. For example, the NLPcomponent 112 or the content selector component 118, can be a singlecomponent, app, or program, or a logic device having one or moreprocessing circuits, or part of one or more servers of the dataprocessing system 102.

Having now described some illustrative implementations, it is apparentthat the foregoing is illustrative and not limiting, having beenpresented by way of example. In particular, although many of theexamples presented herein involve specific combinations of method actsor system elements, those acts and those elements may be combined inother ways to accomplish the same objectives. Acts, elements andfeatures discussed in connection with one implementation are notintended to be excluded from a similar role in other implementations orimplementations.

The phraseology and terminology used herein is for the purpose ofdescription and should not be regarded as limiting. The use of“including” “comprising” “having” “containing” “involving”“characterized by” “characterized in that” and variations thereofherein, is meant to encompass the items listed thereafter, equivalentsthereof, and additional items, as well as alternate implementationsconsisting of the items listed thereafter exclusively. In oneimplementation, the systems and methods described herein consist of one,each combination of more than one, or all of the described elements,acts, or components.

Any references to implementations or elements or acts of the systems andmethods herein referred to in the singular may also embraceimplementations including a plurality of these elements, and anyreferences in plural to any implementation or element or act herein mayalso embrace implementations including only a single element. Referencesin the singular or plural form are not intended to limit the presentlydisclosed systems or methods, their components, acts, or elements tosingle or plural configurations. References to any act or element beingbased on any information, act or element may include implementationswhere the act or element is based at least in part on any information,act, or element.

Any implementation disclosed herein may be combined with any otherimplementation or embodiment, and references to “an implementation,”“some implementations,” “one implementation” or the like are notnecessarily mutually exclusive and are intended to indicate that aparticular feature, structure, or characteristic described in connectionwith the implementation may be included in at least one implementationor embodiment. Such terms as used herein are not necessarily allreferring to the same implementation. Any implementation may be combinedwith any other implementation, inclusively or exclusively, in any mannerconsistent with the aspects and implementations disclosed herein.

References to “or” may be construed as inclusive so that any termsdescribed using “or” may indicate any of a single, more than one, andall of the described terms. For example, a reference to “at least one of‘A’ and ‘B’” can include only ‘A’, only ‘B’, as well as both ‘A’ and‘B’. Such references used in conjunction with “comprising” or other openterminology can include additional items.

Where technical features in the drawings, detailed description or anyclaim are followed by reference signs, the reference signs have beenincluded to increase the intelligibility of the drawings, detaileddescription, and claims. Accordingly, neither the reference signs northeir absence have any limiting effect on the scope of any claimelements.

The systems and methods described herein may be embodied in otherspecific forms without departing from the characteristics thereof. Forexample, the data processing system 102 can select a content item for asubsequent action (e.g., for the third action 215) based in part on datafrom a prior action in the sequence of actions of the thread 200, suchas data from the second action 210 indicating that the second action 210is complete or about to begin. The foregoing implementations areillustrative rather than limiting of the described systems and methods.Scope of the systems and methods described herein is thus indicated bythe appended claims, rather than the foregoing description, and changesthat come within the meaning and range of equivalency of the claims areembraced therein.

What is claimed is:
 1. A system to validate modification of computerprogram output, comprising: a data processing system having one or moreprocessors and memory to: establish a communication channel with athird-party server that provides a computer program comprising achatbot, the computer program comprising the chatbot selected based onan acoustic signal detected by a microphone of a computing device;receive, from the third-party server, a request for content in aparameterized format configured for a parametrically driven text tospeech technique, the request triggered by identification of aplaceholder field in a dialog data structure identified by the chatbot;select, via a content selection process responsive to the request, acontent item for insertion into the placeholder field of the dialog datastructure, the content item in the parameterized format configured forthe parametrically driven text to speech technique; transmit, to thethird-party server different from the data processing system, thecontent item in the parameterized format selected via the contentselection process for provision to the chatbot to cause the computingdevice to perform the parametrically driven text to speech technique togenerate a second acoustic signal corresponding to the dialog datastructure modified with the content item; receive, from the chatbot, anindication of the content item; determine, based on a comparison of theindication of the content item with the content item, that the contentitem received by the chatbot from the third-party server matches thecontent item transmitted by the data processing system to thethird-party server; and set, based on the determination that the contentitem received by the chatbot from the third-party server matches thecontent item transmitted by the data processing system to thethird-party server, a validation parameter for the third-party server.2. The system of claim 1, comprising the data processing system to: pingthe chatbot subsequent to transmission of the content item; and receive,from the chatbot, the indication of the content item responsive to theping.
 3. The system of claim 1, comprising the data processing systemto: ping, subsequent to transmission of the content item, the chatbotbased on a timing function; and receive, from the chatbot, theindication of the content item responsive to the ping.
 4. The system ofclaim 1, comprising the data processing system to: receive an indicationof a second content item from the chatbot; determine, based on acomparison, that the indication of the second content item received fromthe chatbot does not match the content item transmitted by the dataprocessing system to the third-party server; and set the validationparameter based on the determination.
 5. The system of claim 1,comprising the data processing system to: receive an indication of asecond content item from the chatbot; determine, based on a comparison,that the indication of the second content item received from the chatbotdoes not match the content item transmitted by the data processingsystem to the third-party server; and disable, based on thedetermination, provision of content items to chatbots provided by thethird-party server.
 6. The system of claim 1, comprising the dataprocessing system to: determine, based on the comparison, that theindication of the content item received from the chatbot matches thecontent item transmitted by the data processing system to thethird-party server; and record, responsive to the determination, networkactivity information associated with an interaction of the content itemvia the computing device.
 7. The system of claim 1, comprising the dataprocessing system to: encrypt the content item with a hash function andsalts to generate an encrypted content item; and transmit the encryptedcontent item to the third-party server.
 8. The system of claim 1,comprising: encrypt the content item with a hash function and salts togenerate an encrypted content item; and transmit the encrypted contentitem to the third-party server.
 9. The system of claim 1, comprising thedata processing system to: determine, based on an identifier of thecomputing device and via a lookup in a data repository, that thecomputing device is authorized to access the computer program comprisingthe chatbot; and select the content item responsive to the determinationthat the computing device is authorized to access the chatbot.
 10. Amethod of validating a modification of computer program output,comprising: establishing, by a data processing system, a communicationchannel with a third-party server that provides a computer programcomprising a chatbot, the computer program comprising the chatbotselected based on an acoustic signal detected by a microphone of acomputing device; receiving, by the data processing system from thethird-party server, a request for content in a parameterized formatconfigured for a parametrically driven text to speech technique, therequest triggered by identification of a placeholder field in a dialogdata structure identified by the chatbot; selecting, by the dataprocessing system via a content selection process responsive to therequest, a content item for insertion into the placeholder field of thedialog data structure, the content item in the parameterized formatconfigured for the parametrically driven text to speech technique;transmitting, by the data processing system to the third-party server,the content item in the parameterized format selected via the contentselection process for provision to the chatbot to cause the computingdevice to perform the parametrically driven text to speech technique togenerate a second acoustic signal corresponding to the dialog datastructure modified with the content item; receiving, by the dataprocessing system from the chatbot, an indication of the content item;determining, by the data processing system based on a comparison of theindication of the content item with the content item, that the contentitem received by the chatbot from the third-party server matches thecontent item transmitted by the data processing system to thethird-party server; and setting, by the data processing system, based onthe determination that the content item received by the chatbot from thethird-party server matches the content item transmitted by the dataprocessing system to the third-party server, a validation parameter forthe third-party server.
 11. The method of claim 10, comprising: pingingthe chatbot subsequent to transmission of the content item; andreceiving, from the chatbot, the indication of the content itemresponsive to the pinging.
 12. The method of claim 10, comprising:pinging, subsequent to transmission of the content item, the chatbotbased on a timing function; and receiving, from the chatbot, theindication of the content item responsive to the pinging.
 13. The methodof claim 10, comprising: receiving, by the data processing system, anindication of a second content item from the chatbot; determining, basedon a comparison, that the indication of the second content item receivedfrom the chatbot does not match the content item transmitted by the dataprocessing system to the third-party server; and setting the validationparameter based on the determination.
 14. The method of claim 10,comprising: receiving, by the data processing system, an indication of asecond content item from the chatbot; determining, based on acomparison, that the indication of the second content item received fromthe chatbot does not match the content item transmitted by the dataprocessing system to the third-party server; and disabling, based on thedetermination, provision of content items to chatbots provided by thethird-party server.
 15. The method of claim 10, comprising: determining,based on the comparison, that the indication of the content itemreceived from the chatbot matches the content item transmitted by thedata processing system to the third-party server; and recording,responsive to the determination, network activity information associatedwith an interaction of the content item via the computing device. 16.The method of claim 10, comprising: encrypting the content item with ahash function and salts to generate an encrypted content item; andtransmitting the encrypted content item to the third-party server. 17.The method of claim 10, comprising: encrypting the content item with ahash function and salts to generate an encrypted content item; andtransmitting the encrypted content item to the third-party server. 18.The method of claim 10, comprising: determining, based on an identifierof the computing device and via a lookup in a data repository, that thecomputing device is authorized to access the computer program comprisingthe chatbot; and selecting the content item responsive to thedetermination that the computing device is authorized to access thechatbot.